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Takahashi, Adriana. "M?quina de vetores-suporte intervalar." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15225.
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Previous issue date: 2012-09-26The Support Vector Machines (SVM) has attracted increasing attention in machine learning area, particularly on classification and patterns recognition. However, in some cases it is not easy to determinate accurately the class which given pattern belongs. This thesis involves the construction of a intervalar pattern classifier using SVM in association with intervalar theory, in order to model the separation of a pattern set between distinct classes with precision, aiming to obtain an optimized separation capable to treat imprecisions contained in the initial data and generated during the computational processing. The SVM is a linear machine. In order to allow it to solve real-world problems (usually nonlinear problems), it is necessary to treat the pattern set, know as input set, transforming from nonlinear nature to linear problem. The kernel machines are responsible to do this mapping. To create the intervalar extension of SVM, both for linear and nonlinear problems, it was necessary define intervalar kernel and the Mercer s theorem (which caracterize a kernel function) to intervalar function
As m?quinas de vetores suporte (SVM - Support Vector Machines) t?m atra?do muita aten??o na ?rea de aprendizagem de m?quinas, em especial em classifica??o e reconhecimento de padr?es, por?m, em alguns casos nem sempre ? f?cil classificar com precis?o determinados padr?es entre classes distintas. Este trabalho envolve a constru??o de um classificador de padr?es intervalar, utilizando a SVM associada com a teoria intervalar, de modo a modelar com uma precis?o controlada a separa??o entre classes distintas de um conjunto de padr?es, com o objetivo de obter uma separa??o otimizada tratando de imprecis?es contidas nas informa??es do conjunto de padr?es, sejam nos dados iniciais ou erros computacionais. A SVM ? uma m?quina linear, e para que ela possa resolver problemas do mundo real, geralmente problemas n?o lineares, ? necess?rio tratar o conjunto de padr?es, mais conhecido como conjunto de entrada, de natureza n?o linear para um problema linear, as m?quinas kernels s?o respons?veis por esse mapeamento. Para a extens?o intervalar da SVM, tanto para problemas lineares quanto n?o lineares, este trabalho introduz a defini??o de kernel intervalar, bem como estabelece o teorema que valida uma fun??o ser um kernel, o teorema de Mercer para fun??es intervalares
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Tsang, Wai-Hung. "Scaling up support vector machines /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20TSANG.
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Shilton, Alistair. "Design and training of support vector machines." Connect to thesis, 2006. http://repository.unimelb.edu.au/10187/443.
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In this thesis I introduce a new and novel form of SVM known as regression with inequalities, in addition to the standard SVM formulations of binary classification and regression. This extension encompasses both binary classification and regression, reducing the workload when extending the general form; and also provides theoretical insight into the underlying connections between the two formulations.
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Nguyen, Van Toi. "Visual interpretation of hand postures for human-machine interaction." Thesis, La Rochelle, 2015. http://www.theses.fr/2015LAROS035/document.
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Aujourd'hui, les utilisateurs souhaitent interagir plus naturellement avec les systèmes numériques. L'une des modalités de communication la plus naturelle pour l'homme est le geste de la main. Parmi les différentes approches que nous pouvons trouver dans la littérature, celle basée sur la vision est étudiée par de nombreux chercheurs car elle ne demande pas de porter de dispositif complémentaire. Pour que la machine puisse comprendre les gestes à partir des images RGB, la reconnaissance automatique de ces gestes est l'un des problèmes clés. Cependant, cette approche présente encore de multiples défis tels que le changement de point de vue, les différences d'éclairage, les problèmes de complexité ou de changement d'environnement. Cette thèse propose un système de reconnaissance de gestes statiques qui se compose de deux phases : la détection et la reconnaissance du geste lui-même. Dans l'étape de détection, nous utilisons un processus de détection d'objets de Viola Jones avec une caractérisation basée sur des caractéristiques internes d'Haar-like et un classifieur en cascade AdaBoost. Pour éviter l'influence du fond, nous avons introduit de nouvelles caractéristiques internes d'Haar-like. Ceci augmente de façon significative le taux de détection de la main par rapport à l'algorithme original. Pour la reconnaissance du geste, nous avons proposé une représentation de la main basée sur un noyau descripteur KDES (Kernel Descriptor) très efficace pour la classification d'objets. Cependant, ce descripteur n'est pas robuste au changement d'échelle et n'est pas invariant à l'orientation. Nous avons alors proposé trois améliorations pour surmonter ces problèmes : i) une normalisation de caractéristiques au niveau pixel pour qu'elles soient invariantes à la rotation ; ii) une génération adaptative de caractéristiques afin qu'elles soient robustes au changement d'échelle ; iii) une construction spatiale spécifique à la structure de la main au niveau image. Sur la base de ces améliorations, la méthode proposée obtient de meilleurs résultats par rapport au KDES initial et aux descripteurs existants. L'intégration de ces deux méthodes dans une application montre en situation réelle l'efficacité, l'utilité et la faisabilité de déployer un tel système pour l'interaction homme-robot utilisant les gestes de la mainNowadays, people want to interact with machines more naturally. One of the powerful communication channels is hand gesture. Vision-based approach has involved many researchers because this approach does not require any extra device. One of the key problems we need to resolve is hand posture recognition on RGB images because it can be used directly or integrated into a multi-cues hand gesture recognition. The main challenges of this problem are illumination differences, cluttered background, background changes, high intra-class variation, and high inter-class similarity. This thesis proposes a hand posture recognition system consists two phases that are hand detection and hand posture recognition. In hand detection step, we employed Viola-Jones detector with proposed concept Internal Haar-like feature. The proposed hand detection works in real-time within frames captured from real complex environments and avoids unexpected effects of background. The proposed detector outperforms original Viola-Jones detector using traditional Haar-like feature. In hand posture recognition step, we proposed a new hand representation based on a good generic descriptor that is kernel descriptor (KDES). When applying KDES into hand posture recognition, we proposed three improvements to make it more robust that are adaptive patch, normalization of gradient orientation in patches, and hand pyramid structure. The improvements make KDES invariant to scale change, patch-level feature invariant to rotation, and final hand representation suitable to hand structure. Based on these improvements, the proposed method obtains better results than original KDES and a state of the art method
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Karode, Andrew. "Support vector machine classification of network streams using a spectrum kernel encoding." Winston-Salem, NC : Wake Forest University, 2008. http://dspace.zsr.wfu.edu/jspui/handle/10339/38157.
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Thesis (M.S.)--Wake Forest University. Dept. of Computer Science, 2008.Title from electronic thesis title page. Thesis advisor: William H. Turkett Jr. Includes bibliographical references (p. 61-65).
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Duman, Asli. "Multiple Criteria Sorting Methods Based On Support Vector Machines." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612863/index.pdf.
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This study addresses sorting problems with predefined ordinal classes. We develop a new method based on Support Vector Machine (SVM) model, which is mainly
used for nominal binary or multi-class classification processes. In the proposed method, the SVM model is extended to include the preferences of the decision
maker and the ordinal relationship between classes in sorting problems. New sets of constraints are added to the SVM model. We demonstrate the performance of the
proposed method through several data sets. We compare the results with those of classical SVM model and UTADIS method, a well-known multiple criteria sorting method. We also analyze the effect of feature space mapping by Kernel Trick utilization on the results.
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Westin, Emil. "Authorship classification using the Vector Space Model and kernel methods." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412897.
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Authorship identification is the field of classifying a given text by its author based on the assumption that authors exhibit unique writing styles. This thesis investigates the semantic shortcomings of the vector space model by constructing a semantic kernel created from WordNet which is evaluated on the problem of authorship attribution. A multiclass SVM classifier is constructed using the one-versus-all strategy and evaluated in terms of precision, recall, accuracy and F1 scores. Results show that the use of the semantic scores from WordNet degrades the performance compared to using a linear kernel. Experiments are run to identify the best feature engineering configurations, showing that removing stopwords has a positive effect on the financial dataset Reuters while the Kaggle dataset consisting of short extracts of horror stories benefit from keeping the stopwords.
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Luo, Tong. "Scaling up support vector machines with application to plankton recognition." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001154.
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Pilkington, Nicholas Charles Victor. "Hyperparameter optimisation for multiple kernels." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648763.
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Wang, Zhuang. "Budgeted Online Kernel Classifiers for Large Scale Learning." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/89554.
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Computer and Information SciencePh.D.
In the environment where new large scale problems are emerging in various disciplines and pervasive computing applications are becoming more common, there is an urgent need for machine learning algorithms that could process increasing amounts of data using comparatively smaller computing resources in a computational efficient way. Previous research has resulted in many successful learning algorithms that scale linearly or even sub-linearly with sample size and dimension, both in runtime and in space. However, linear or even sub-linear space scaling is often not sufficient, because it implies an unbounded growth in memory with sample size. This clearly opens another challenge: how to learn from large, or practically infinite, data sets or data streams using memory limited resources. Online learning is an important learning scenario in which a potentially unlimited sequence of training examples is presented one example at a time and can only be seen in a single pass. This is opposed to offline learning where the whole collection of training examples is at hand. The objective is to learn an accurate prediction model from the training stream. Upon on repetitively receiving fresh example from stream, typically, online learning algorithms attempt to update the existing model without retraining. The invention of the Support Vector Machines (SVM) attracted a lot of interest in adapting the kernel methods for both offline and online learning. Typical online learning for kernel classifiers consists of observing a stream of training examples and their inclusion as prototypes when specified conditions are met. However, such procedure could result in an unbounded growth in the number of prototypes. In addition to the danger of the exceeding the physical memory, this also implies an unlimited growth in both update and prediction time. To address this issue, in my dissertation I propose a series of kernel-based budgeted online algorithms, which have constant space and constant update and prediction time. This is achieved by maintaining a fixed number of prototypes under the memory budget. Most of the previous works on budgeted online algorithms focus on kernel perceptron. In the first part of the thesis, I review and discuss these existing algorithms and then propose a kernel perceptron algorithm which removes the prototype with the minimal impact on classification accuracy to maintain the budget. This is achieved by dual use of cached prototypes for both model presentation and validation. In the second part, I propose a family of budgeted online algorithms based on the Passive-Aggressive (PA) style. The budget maintenance is achieved by introducing an additional constraint into the original PA optimization problem. A closed-form solution was derived for the budget maintenance and model update. In the third part, I propose a budgeted online SVM algorithm. The proposed algorithm guarantees that the optimal SVM solution is maintained on all the prototype examples at any time. To maximize the accuracy, prototypes are constructed to approximate the data distribution near the decision boundary. In the fourth part, I propose a family of budgeted online algorithms for multi-class classification. The proposed algorithms are the recently proposed SVM training algorithm Pegasos. I prove that the gap between the budgeted Pegasos and the optimal SVM solution directly depends on the average model degradation due to budget maintenance. Following the analysis, I studied greedy multi-class budget maintenance methods based on removal, projection and merging of SVs. In each of these four parts, the proposed algorithms were experimentally evaluated against the state-of-art competitors. The results show that the proposed budgeted online algorithms outperform the competitive algorithm and achieve accuracy comparable to non-budget counterparts while being extremely computationally efficient.
Temple University--Theses
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Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.
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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM.
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Zwald, Laurent. "Performances statistiques d'algorithmes d'apprentissage : "Kernel projection machine" et analyse en composantes principales à noyau." Paris 11, 2005. https://tel.archives-ouvertes.fr/tel-00012011.
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La thèse se place dans le cadre de l'apprentissage statistique. Elle apporte des contributions à la communauté du machine learning en utilisant des techniques de statistiques modernes basées sur des avancées dans l'étude des processus empiriques. Dans une première partie, les propriétés statistiques de l'analyse en composantes principales a noyau (KPCA) sont explorées. Le comportement de l'erreur de reconstruction est étudie avec un point de vue non-asymptotique et des inégalités de concentration des valeurs propres de la matrice de Gram sont données. Tous ces résultats impliquent des vitesses de convergence rapides. Des propriétés non-asymptotiques concernant les espaces propres de la KPCA eux-mêmes sont également proposées. Dans une deuxième partie, un nouvel algorithme de classification a été conçu : la Kernel Projection Machine (KPM). Tout en s'inspirant des Support Vector Machines (SVM), il met en lumière que la sélection d'un espace vectoriel par une méthode de réduction de la dimension telle que la KPCA régularise convenablement. Le choix de l'espace vectoriel utilise par la KPM est guide par des études statistiques de sélection de modèle par minimisation pénalisée de la perte empirique. Ce principe de régularisation est étroitement relie a la projection fini-dimensionnelle étudiée dans les travaux statistiques de Birge et Massart. Les performances de la KPM et de la SVM sont ensuite comparées sur différents jeux de données. Chaque thème aborde dans cette thèse soulevé de nouvelles questions d'ordre théorique et pratiqueThis thesis takes place within the framework of statistical learning. It brings contributions to the machine learning community using modern statistical techniques based on progress in the study of empirical processes. The first part investigates the statistical properties of Kernel Principal Component Analysis (KPCA). The behavior of the reconstruction error is studied with a non-asymptotique point of view and concentration inequalities of the eigenvalues of the kernel matrix are provided. All these results correspond to fast convergence rates. Non-asymptotic results concerning the eigenspaces of KPCA themselves are also provided. A new algorithm of classification has been designed in the second part: the Kernel Projection Machine (KPM). It is inspired by the Support Vector Machines (SVM). Besides, it highlights that the selection of a vector space by a dimensionality reduction method such as KPCA regularizes suitably. The choice of the vector space involved in the KPM is guided by statistical studies of model selection using the penalized minimization of the empirical loss. This regularization procedure is intimately connected with the finite dimensional projections studied in the statistical work of Birge and Massart. The performances of KPM and SVM are then compared on some data sets. Each topic tackled in this thesis raises new questions
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Vishwanathan, S. V. N. "Kernel Methods Fast Algorithms and real life applications." Thesis, Indian Institute of Science, 2003. http://hdl.handle.net/2005/49.
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Support Vector Machines (SVM) have recently gained prominence in the field of machine learning and pattern classification (Vapnik, 1995, Herbrich, 2002, Scholkopf and Smola, 2002). Classification is achieved by finding a separating hyperplane in a feature space, which can be mapped back onto a non-linear surface in the input space. However, training an SVM involves solving a quadratic optimization problem, which tends to be computationally intensive. Furthermore, it can be subject to stability problems and is non-trivial to implement. This thesis proposes an fast iterative Support Vector training algorithm which overcomes some of these problems.
Our algorithm, which we christen Simple SVM, works mainly for the quadratic soft margin loss (also called the l2 formulation). We also sketch an extension for the linear soft-margin loss (also called the l1 formulation). Simple SVM works by incrementally changing a candidate Support Vector set using a locally greedy approach, until the supporting hyperplane is found within a finite number of iterations. It is derived by a simple (yet computationally crucial) modification of the incremental SVM training algorithms of Cauwenberghs and Poggio (2001) which allows us to perform update operations very efficiently. Constant-time methods for initialization of the algorithm and experimental evidence for the speed of the proposed algorithm, when compared to methods such as Sequential Minimal Optimization and the Nearest Point Algorithm are given. We present results on a variety of real life datasets to validate our claims.
In many real life applications, especially for the l2 formulation, the kernel matrix K є R n x n can be written as
K = Z T Z + Λ ,
where, Z є R n x m with m << n and Λ є R n x n is diagonal with nonnegative entries. Hence the matrix K - Λ is rank-degenerate, Extending the work of Fine and Scheinberg (2001) and Gill et al. (1975) we propose an efficient factorization algorithm which can be used to find a L D LT factorization of K in 0(nm2) time. The modified factorization, after a rank one update of K, can
be computed in 0(m2) time. We show how the Simple SVM algorithm can be sped up by taking advantage of this new factorization. We also demonstrate applications of our factorization to interior point methods. We show a close relation between the LDV factorization of a rectangular matrix and our LDLT factorization (Gill et al., 1975).
An important feature of SVM's is that they can work with data from any input domain as long as a suitable mapping into a Hilbert space can be found, in other words, given the input data we should be able to compute a positive semi definite kernel matrix of the data (Scholkopf and Smola, 2002). In this thesis we propose kernels on a variety of discrete objects, such as strings, trees, Finite State Automata, and Pushdown Automata. We show that our kernels include as special cases the celebrated Pair-HMM kernels (Durbin et al., 1998, Watkins, 2000), the spectrum kernel (Leslie et al., 20024, convolution kernels for NLP (Collins and Duffy, 2001), graph diffusion kernels (Kondor and Lafferty, 2002) and various other string-matching kernels.
Because of their widespread applications in bio-informatics and web document based algorithms, string kernels are of special practical importance. By intelligently using the matching statistics algorithm of Chang and Lawler (1994), we propose, perhaps, the first ever algorithm to compute string kernels in linear time. This obviates dynamic programming with quadratic time complexity and makes string kernels a viable alternative for the practitioner. We also propose extensions of our string kernels to compute kernels on trees efficiently. This thesis presents a linear time algorithm for ordered trees and a log-linear time algorithm for unordered trees.
In general, SVM's require time proportional to the number of Support Vectors for prediction. In case the dataset is noisy a large fraction of the data points become Support Vectors and thus time required for prediction increases. But, in many applications like search engines or web document retrieval, the dataset is noisy, yet, the speed of prediction is critical. We propose a method for string kernels by which the prediction time can be reduced to linear in the length of the sequence to be classified, regardless of the number of Support Vectors. We achieve this by using a weighted version of our string kernel algorithm.
We explore the relationship between dynamic systems and kernels. We define kernels on various kinds of dynamic systems including Markov chains (both discrete and continuous), diffusion processes on graphs and Markov chains, Finite State Automata, various linear time-invariant systems etc Trajectories arc used to define kernels introduced on initial conditions lying underlying dynamic system. The same idea is extended to define Kernels on a. dynamic system with respect to a set of initial conditions. This framework leads to a large number of novel kernels and also generalize many previously proposed kernels.
Lack of adequate training data is a problem which plagues classifiers. We propose n new method to generate virtual training samples in the case of handwritten digit data. Our method uses the two dimensional suffix tree representation of a set of matrices to encode an exponential number of virtual samples in linear space thus leading to an increase in classification accuracy. This in turn, leads us naturally to a, compact data dependent representation of a test pattern which we call the description tree. We propose a new kernel for images and demonstrate a quadratic time algorithm for computing it by wing the suffix tree representation of an image. We also describe a method to reduce the prediction time to quadratic in the size of the test image by using techniques similar to those used for string kernels.
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Gilani, Syed Hassan. "Road Sign Recognition based onInvariant Features using SupportVector Machine." Thesis, Högskolan Dalarna, Datateknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:du-2760.
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Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.
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Chen, Xiaoyi. "Transfer Learning with Kernel Methods." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0005.
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Le transfert d‘apprentissage regroupe les méthodes permettant de transférer l’apprentissage réalisé sur des données (appelées Source) à des données nouvelles, différentes, mais liées aux données Source. Ces travaux sont une contribution au transfert d’apprentissage homogène (les domaines de représentation des Source et Cible sont identiques) et transductif (la tâche à effectuer sur les données Cible est identique à celle sur les données Source), lorsque nous ne disposons pas d’étiquettes des données Cible. Dans ces travaux, nous relâchons la contrainte d’égalité des lois des étiquettes conditionnellement aux observations, souvent considérée dans la littérature. Notre approche permet de traiter des cas de plus en plus généraux. Elle repose sur la recherche de transformations permettant de rendre similaires les données Source et Cible. Dans un premier temps, nous recherchons cette transformation par Maximum de Vraisemblance. Ensuite, nous adaptons les Machines à Vecteur de Support en intégrant une contrainte additionnelle sur la similitude des données Source et Cible. Cette similitude est mesurée par la Maximum Mean Discrepancy. Enfin, nous proposons l’utilisation de l’Analyse en Composantes Principales à noyau pour rechercher un sous espace, obtenu à partir d’une transformation non linéaire des données Source et Cible, dans lequel les lois des observations sont les plus semblables possibles. Les résultats expérimentaux montrent l’efficacité de nos approchesTransfer Learning aims to take advantage of source data to help the learning task of related but different target data. This thesis contributes to homogeneous transductive transfer learning where no labeled target data is available. In this thesis, we relax the constraint on conditional probability of labels required by covariate shift to be more and more general, based on which the alignment of marginal probabilities of source and target observations renders source and target similar. Thus, firstly, a maximum likelihood based approach is proposed. Secondly, SVM is adapted to transfer learning with an extra MMD-like constraint where Maximum Mean Discrepancy (MMD) measures this similarity. Thirdly, KPCA is used to align data in a RKHS on minimizing MMD. We further develop the KPCA based approach so that a linear transformation in the input space is enough for a good and robust alignment in the RKHS. Experimentally, our proposed approaches are very promising
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Garg, Aditie. "Designing Reactive Power Control Rules for Smart Inverters using Machine Learning." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83558.
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Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid.
Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid services. This thesis uses machine learning tools and poses reactive power control as a kernel-based regression task to learn policies and evaluate the reactive power injections in real-time. This novel approach performs inverter coordination through non-linear control policies centrally designed by the operator on a slower timescale using anticipated scenarios for load and generation. In real-time, the inverters feed locally and/or globally collected grid data to the customized control rules. The developed models are highly adjustable to the available computation and communication resources. The developed control scheme is tested on the IEEE 123-bus system and is seen to efficiently minimize losses and regulate voltage within the permissible limits.Master of Science
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Linton, Thomas. "Forecasting hourly electricity consumption for sets of households using machine learning algorithms." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186592.
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To address inefficiency, waste, and the negative consequences of electricity generation, companies and government entities are looking to behavioural change among residential consumers. To drive behavioural change, consumers need better feedback about their electricity consumption. A monthly or quarterly bill provides the consumer with almost no useful information about the relationship between their behaviours and their electricity consumption. Smart meters are now widely dispersed in developed countries and they are capable of providing electricity consumption readings at an hourly resolution, but this data is mostly used as a basis for billing and not as a tool to assist the consumer in reducing their consumption. One component required to deliver innovative feedback mechanisms is the capability to forecast hourly electricity consumption at the household scale. The work presented by this thesis is an evaluation of the effectiveness of a selection of kernel based machine learning methods at forecasting the hourly aggregate electricity consumption for different sized sets of households. The work of this thesis demonstrates that k-Nearest Neighbour Regression and Gaussian process Regression are the most accurate methods within the constraints of the problem considered. In addition to accuracy, the advantages and disadvantages of each machine learning method are evaluated, and a simple comparison of each algorithms computational performance is made.För att ta itu med ineffektivitet, avfall, och de negativa konsekvenserna av elproduktion så vill företag och myndigheter se beteendeförändringar bland hushållskonsumenter. För att skapa beteendeförändringar så behöver konsumenterna bättre återkoppling när det gäller deras elförbrukning. Den nuvarande återkopplingen i en månads- eller kvartalsfaktura ger konsumenten nästan ingen användbar information om hur deras beteenden relaterar till deras konsumtion. Smarta mätare finns nu överallt i de utvecklade länderna och de kan ge en mängd information om bostäders konsumtion, men denna data används främst som underlag för fakturering och inte som ett verktyg för att hjälpa konsumenterna att minska sin konsumtion. En komponent som krävs för att leverera innovativa återkopplingsmekanismer är förmågan att förutse elförbrukningen på hushållsskala. Arbetet som presenteras i denna avhandling är en utvärdering av noggrannheten hos ett urval av kärnbaserad maskininlärningsmetoder för att förutse den sammanlagda förbrukningen för olika stora uppsättningar av hushåll. Arbetet i denna avhandling visar att "k-Nearest Neighbour Regression" och "Gaussian Process Regression" är de mest exakta metoder inom problemets begränsningar. Förutom noggrannhet, så görs en utvärdering av fördelar, nackdelar och prestanda hos varje maskininlärningsmetod.
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Kingravi, Hassan. "Reduced-set models for improving the training and execution speed of kernel methods." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51799.
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This thesis aims to contribute to the area of kernel methods, which are a class of machine learning methods known for their wide applicability and state-of-the-art performance, but which suffer from high training and evaluation complexity. The work in this thesis utilizes the notion of reduced-set models to alleviate the
training and testing complexities of these methods in a unified manner. In the first part of the thesis, we use recent results in kernel smoothing and integral-operator learning to design a generic strategy to speed up various kernel methods. In Chapter 3, we present a method to speed up kernel PCA (KPCA), which is one of the fundamental kernel methods for manifold learning, by using reduced-set density estimates (RSDE) of the data. The proposed method induces an integral operator that is an approximation of the ideal integral operator associated to KPCA. It is shown that the error between the ideal and approximate integral operators is related to the error between the ideal and approximate kernel density estimates of the data. In Chapter 4, we derive similar approximation algorithms for Gaussian process regression, diffusion maps, and kernel embeddings of conditional distributions. In the second part of the thesis, we use reduced-set models for kernel methods to tackle online learning in model-reference adaptive control (MRAC). In Chapter 5, we relate the properties of the feature spaces induced by Mercer kernels to make a connection between persistency-of-excitation and the budgeted placement of kernels to minimize tracking and modeling error. In Chapter 6, we use a Gaussian process (GP) formulation of the modeling error to accommodate a larger class of errors, and design a reduced-set algorithm to learn a GP model of the modeling error. Proofs of stability for all the algorithms are presented, and simulation results on a challenging control problem validate the methods.
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Díaz, Jorge Luis Guevara. "Modelos de aprendizado supervisionado usando métodos kernel, conjuntos fuzzy e medidas de probabilidade." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03122015-155546/.
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Esta tese propõe uma metodologia baseada em métodos de kernel, teoria fuzzy e probabilidade para tratar conjuntos de dados cujas observações são conjuntos de pontos. As medidas de probabilidade e os conjuntos fuzzy são usados para modelar essas observações. Posteriormente, graças a kernels definidos sobre medidas de probabilidade, ou em conjuntos fuzzy, é feito o mapeamento implícito dessas medidas de probabilidade, ou desses conjuntos fuzzy, para espaços de Hilbert com kernel reproduzível, onde a análise pode ser feita com algum método kernel. Usando essa metodologia, é possível fazer frente a uma ampla gamma de problemas de aprendizado para esses conjuntos de dados. Em particular, a tese apresenta o projeto de modelos de descrição de dados para observações modeladas com medidas de probabilidade. Isso é conseguido graças ao mergulho das medidas de probabilidade nos espaços de Hilbert, e a construção de esferas envolventes mínimas nesses espaços de Hilbert. A tese apresenta como esses modelos podem ser usados como classificadores de uma classe, aplicados na tarefa de detecção de anomalias grupais. No caso que as observações sejam modeladas por conjuntos fuzzy, a tese propõe mapear esses conjuntos fuzzy para os espaços de Hilbert com kernel reproduzível. Isso pode ser feito graças à projeção de novos kernels definidos sobre conjuntos fuzzy. A tese apresenta como esses novos kernels podem ser usados em diversos problemas como classificação, regressão e na definição de distâncias entre conjuntos fuzzy. Em particular, a tese apresenta a aplicação desses kernels em problemas de classificação supervisionada em dados intervalares e teste kernel de duas amostras para dados contendo atributos imprecisos.This thesis proposes a methodology based on kernel methods, probability measures and fuzzy sets, to analyze datasets whose individual observations are itself sets of points, instead of individual points. Fuzzy sets and probability measures are used to model observations; and kernel methods to analyze the data. Fuzzy sets are used when the observation contain imprecise, vague or linguistic values. Whereas probability measures are used when the observation is given as a set of multidimensional points in a $D$-dimensional Euclidean space. Using this methodology, it is possible to address a wide range of machine learning problems for such datasets. Particularly, this work presents data description models when observations are modeled by probability measures. Those description models are applied to the group anomaly detection task. This work also proposes a new class of kernels, \\emph{the kernels on fuzzy sets}, that are reproducing kernels able to map fuzzy sets to a geometric feature spaces. Those kernels are similarity measures between fuzzy sets. We give from basic definitions to applications of those kernels in machine learning problems as supervised classification and a kernel two-sample test. Potential applications of those kernels include machine learning and patter recognition tasks over fuzzy data; and computational tasks requiring a similarity measure estimation between fuzzy sets.
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Franchi, Gianni. "Machine learning spatial appliquée aux images multivariées et multimodales." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEM071/document.
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Cette thèse porte sur la statistique spatiale multivariée et l’apprentissage appliqués aux images hyperspectrales et multimodales. Les thèmes suivants sont abordés :Fusion d'images :Le microscope électronique à balayage (MEB) permet d'acquérir des images à partir d'un échantillon donné en utilisant différentes modalités. Le but de ces études est d'analyser l’intérêt de la fusion de l'information pour améliorer les images acquises par MEB. Nous avons mis en œuvre différentes techniques de fusion de l'information des images, basées en particulier sur la théorie de la régression spatiale. Ces solutions ont été testées sur quelques jeux de données réelles et simulées.Classification spatiale des pixels d’images multivariées :Nous avons proposé une nouvelle approche pour la classification de pixels d’images multi/hyper-spectrales. Le but de cette technique est de représenter et de décrire de façon efficace les caractéristiques spatiales / spectrales de ces images. Ces descripteurs multi-échelle profond visent à représenter le contenu de l'image tout en tenant compte des invariances liées à la texture et à ses transformations géométriques.Réduction spatiale de dimensionnalité :Nous proposons une technique pour extraire l'espace des fonctions en utilisant l'analyse en composante morphologiques. Ainsi, pour ajouter de l'information spatiale et structurelle, nous avons utilisé les opérateurs de morphologie mathématiqueThis thesis focuses on multivariate spatial statistics and machine learning applied to hyperspectral and multimodal and images in remote sensing and scanning electron microscopy (SEM). In this thesis the following topics are considered:Fusion of images:SEM allows us to acquire images from a given sample using different modalities. The purpose of these studies is to analyze the interest of fusion of information to improve the multimodal SEM images acquisition. We have modeled and implemented various techniques of image fusion of information, based in particular on spatial regression theory. They have been assessed on various datasets.Spatial classification of multivariate image pixels:We have proposed a novel approach for pixel classification in multi/hyper-spectral images. The aim of this technique is to represent and efficiently describe the spatial/spectral features of multivariate images. These multi-scale deep descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations.Spatial dimensionality reduction:We have developed a technique to extract a feature space using morphological principal component analysis. Indeed, in order to take into account the spatial and structural information we used mathematical morphology operators
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Ashrafi, Parivash. "Predicting the absorption rate of chemicals through mammalian skin using machine learning algorithms." Thesis, University of Hertfordshire, 2016. http://hdl.handle.net/2299/17310.
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Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This thesis evaluates the application of these methods to the problem domain of skin permeability. ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. Historically, refining mathematical models used to predict percutaneous drug absorption has been thought of as a key factor in this field. Quantitative Structure-Activity Relationships (QSARs) models are used extensively for this purpose. However, advanced ML methods successfully outperform the traditional linear QSAR models. In this thesis, the application of ML methods to percutaneous absorption are investigated and evaluated. The major approach used in this thesis is Gaussian process (GP) regression method. This research seeks to enhance the prediction performance by using local non-linear models obtained from applying clustering algorithms. In addition, to increase the model's quality, a kernel is generated based on both numerical chemical variables and categorical experimental descriptors. Monte Carlo algorithm is also employed to generate reliable models from variable data which is inevitable in biological experiments. The datasets used for this study are small and it may raise the over-fitting/under-fitting problem. In this research I attempt to find optimal values of skin permeability using GP optimisation algorithms within small datasets. Although these methods are applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.
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Abo, Al Ahad George, and Abbas Salami. "Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets." Thesis, Linköpings universitet, Produktionsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.
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Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging areas of finance. Having an immense variety of economic data, stakeholders aim to understand the current and future state of the market. Since it is hard for a human to make sense out of large amounts of data, different modeling techniques have been applied to extract useful information from financial databases, where machine learning techniques are among the most recent modeling techniques. Binary classifiers such as Support Vector Machines (SVMs) have to some extent been used for this purpose where extensions of the algorithm have been developed with increased prediction performance as the main goal. The objective of this study has been to develop a process for improving the performance when predicting the sign of return of financial time series with soft margin classifiers. An analysis regarding the algorithms is presented in this study followed by a description of the methodology that has been utilized. The developed process containing some of the presented soft margin classifiers, and other aspects of kernel methods such as Multiple Kernel Learning have shown pleasant results over the long term, in which the capability of capturing different market conditions have been shown to improve with the incorporation of different models and kernels, instead of only a single one. However, the results are mostly congruent with earlier studies in this field. Furthermore, two research questions have been answered where the complexity regarding the kernel functions that are used by the SVM have been studied and the robustness of the process as a whole. Complexity refers to achieving more complex feature maps through combining kernels by either adding, multiplying or functionally transforming them. It is not concluded that an increased complexity leads to a consistent improvement, however, the combined kernel function is superior during some of the periods of the time series used in this thesis for the individual models. The robustness has been investigated for different signal-to-noise ratio where it has been observed that windows with previously poor performance are more exposed to noise impact.
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Wehmann, Adam. "A Spatial-Temporal Contextual Kernel Method for Generating High-Quality Land-Cover Time Series." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398866264.
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Sangnier, Maxime. "Outils d'apprentissage automatique pour la reconnaissance de signaux temporels." Rouen, 2015. http://www.theses.fr/2015ROUES064.
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Les travaux présentés ici couvrent deux thématiques de la reconnaissance de signaux temporels par des systèmes numériques dont certains paramètres sont inférés à partir d’un ensemble limité d’observations. La première est celle de la détermination automatique de caractéristiques discriminantes. Pour ce faire, nous proposons un algorithme de génération de colonnes capable d’apprendre une transformée Temps-Fréquence (TF), mise sous la forme d’un banc de filtres, de concert à une machine à vecteurs supports. Cet algorithme est une extension des techniques existantes d’apprentissage de noyaux multiples, combinant de manière non-linéaire une infinité de noyaux. La seconde thématique dans laquelle s’inscrivent nos travaux est l’appréhension de la temporalité des signaux. Si cette notion a été abordée au cours de notre première contribution, qui a pointé la nécessité de déterminer au mieux la résolution temporelle d’une représentation TF, elle prend tout son sens dans une prise de décision au plus tôt. Dans ce contexte, notre seconde contribution fournit un cadre méthodologique permettant de détecter précocement un événement particulier au sein d’une séquence, c’est à dire avant que ledit événement ne se termine. Celui-ci est construit comme une extension de l’apprentissage d’instances multiples et des espaces de similarité aux séries temporelles. De plus, nous accompagnons cet outil d’un algorithme d’apprentissage efficace et de garanties théoriques de généralisation. L’ensemble de nos travaux a été évalué sur des signaux issus d’interfaces cerveau-machine, des paysages sonores et des vidéos représentant des actions humainesThe work presented here tackles two different subjects in the wide thematic of how to build a numerical system to recognize temporal signals, mainly from limited observations. The first one is automatic feature extraction. For this purpose, we present a column generation algorithm, which is able to jointly learn a discriminative Time-Frequency (TF) transform, cast as a filter bank, with a support vector machine. This algorithm extends the state of the art on multiple kernel learning by non-linearly combining an infinite amount of kernels. The second direction of research is the way to handle the temporal nature of the signals. While our first contribution pointed out the importance of correctly choosing the time resolution to get a discriminative TF representation, the role of the time is clearly enlightened in early recognition of signals. Our second contribution lies in this field and introduces a methodological framework for early detection of a special event in a time-series, that is detecting an event before it ends. This framework builds upon multiple instance learning and similarity spaces by fitting them to the particular case of temporal sequences. Furthermore, our early detector comes with an efficient learning algorithm and theoretical guarantees on its generalization ability. Our two contributions have been empirically evaluated with brain-computer interface signals, soundscapes and human actions movies
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Louradour, Jérôme. "Noyaux de séquences pour la vérification du locuteur par machines à vecteurs de support." Toulouse 3, 2007. http://www.theses.fr/2007TOU30004.
Full textAbstract:
La vérification automatique du locuteur (VAL) est une tâche de classification binaire, qui consiste à déterminer si un énoncé de parole a été prononcé ou non par un locuteur cible. Les Machines à Vecteurs de Support (SVMs) sont devenues un outil classique pour ce type de classification. Cette approche discriminante a suscité l’intérêt de nombreuses recherches en reconnaissance des formes, tant pour ses fondements théoriques solides que pour ses bonnes performances empiriques. Mais la mise en oeuvre des SVMs pour la VAL en situation réelle soulevant plusieurs problèmes relatifs aux caractéristiques propres à cette tâche. Il s’agit principalement de la taille élevée des corpus d’apprentissage et de la nature séquentielle des observations à classifier. Cette thèse est consacrée à l’exploration des noyaux de séquences pour la classification SVM du locuteur. Nous commen¸cons par faire un tour d’horizon des méthodes émergentes pour construire des noyaux de séquences. Ensuite nous proposons une nouvelle famille de noyaux en se basant sur une généralisation d’un noyau qui a fait ses preuves en VAL. Nous faisons l’analyse théorique et algorithmique de cette nouvelle famille avant de l’appliquer à la VAL par SVM. Après la mise en oeuvre des systèmes SVMs à base des différents noyaux que nous avons étudiés, nous comparons leurs performances sur le corpus NIST SRE 2005, à partir d’un protocole de développement commun. Enfin, nous introduisons un nouveau concept pour aborder le problème de VAL, dont le principe est de déterminer si deux séquences ont été prononcées par le même locuteur. L’utilisation des SVMs pour exploiter ce concept nous amène à définir une nouvelle catégorie de noyaux : les noyaux entre paires de séquencesThis thesis is focused on the application of Support Vector Machines (SVM) to Automatic Text-Independent Speaker Verification. This speech processing task consists in determining whether a speech utterance was pronounced or not by a target speaker, without any constraint on the speech content. In order to apply a kernel method such as SVM to this binary classification of variable-length sequences, an appropriate approach is to use kernels that can handle sequences, and not acoustic vectors within sequences. As explained in the thesis report, both theoretical and practical reasons justify the effort of searching such kernels. The present study concentrates in exploring several aspects of kernels for sequences, and in applying them to a very large database speaker verification problem under realistic recording conditions. After reviewing emergent methods to conceive sequence kernels and presenting them in a unified framework, we propose a new family of such kernels : the Feature Space Normalized Sequence (FSNS) kernels. These kernels are a generalization of the GLDS kernel, which is now well-known for its efficiency in speaker verification. A theoretical and algorithmic study of FSNS kernels is carried out. In particular, several forms are introduced and justified, and a sparse greedy matrix approximation method is used to suggest an efficient and suitable implementation of FSNS kernels for speaker verification. .
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Behúň, Kamil. "Příznaky z videa pro klasifikaci." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236367.
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This thesis compares hand-designed features with features learned by feature learning methods in video classification. The features learned by Principal Component Analysis whitening, Independent subspace analysis and Sparse Autoencoders were tested in a standard Bag of Visual Word classification paradigm replacing hand-designed features (e.g. SIFT, HOG, HOF). The classification performance was measured on Human Motion DataBase and YouTube Action Data Set. Learned features showed better performance than the hand-desined features. The combination of hand-designed features and learned features by Multiple Kernel Learning method showed even better performance, including cases when hand-designed features and learned features achieved not so good performance separately.
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Abdallah, Fahed. "Noyaux reproduisants et critères de contraste pour l'élaboration de détecteurs à structure imposée." Troyes, 2004. http://www.theses.fr/2004TROY0002.
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Les travaux réalisés pendant cette thèse sont relatifs à la synthèse de détecteurs à partir d'une base d'exemples étiquetés. La théorie développée fait appel aux espaces de Hilbert à noyaux reproduisants pour l'élaboration de détecteurs linéaires généralisés dans des espaces transformés de dimension importante, voire infinie, sans qu'aucun calcul n'y soit effectué explicitement. Elle repose sur l'optimisation du meilleur critère de contraste pour le problème traité, après s'être assuré que de telles mesures de performance permettant l'obtention sous des conditions restrictives assez faibles, à une statistique équivalente au rapport de vraisemblance. Pour une meilleure prise en compte de phénomènes tels que la malédiction de la dimensionnalité, l'approche proposée s'appuie sur la théorie de l'apprentissage. Celle-ci lui permet d'offrir des garanties de performances en généralisation. On propose ainsi des méthodes qui permettent le contrôle de complexité des détecteurs obtenus. Les résultats obtenus sur des données synthétiques et réelles montrent que notre approche est en mesure de rivaliser avec les structures de décision les plus étudiées actuellement que sont les Support Vector MachinesIn this thesis, we consider statistical learning machines with try to infer rules from a given set or observations in order to make correct predictions on unseen examples. Building upon the theory of reproducing kernels, we develop a generalized linear detector in transformed spaces of high dimension, without explicitly doing any calculus in these spaces. The method is based on the optimization of the best second-order criterion with respect to the problem to solve. In fact, theoretical results show that second-order criteria are able, under some mild conditions, to guarantee the best solution in the sense of classical detection theories. Achieving a good generalisation performance with a receiver requires matching its complexity to the amount of available training data. This problem, known as the curse of dimensionality, has been studied theoretically by Vapnik and Chervonenkis. In this dissertation, we propose complexity control procedures in order to improve the performance of these receivers when few training data are available. Simulation results on real and synthetic data show clearly the competitiveness of our approach compared with other state of the art existing kernel methods like Support Vector Machines
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Bhadra, Sahely. "Learning Robust Support Vector Machine Classifiers With Uncertain Observations." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2475.
Full textAbstract:
The central theme of the thesis is to study linear and non linear SVM formulations in the presence of uncertain observations. The main contribution of this thesis is to derive robust classfiers from partial knowledge of the underlying uncertainty.
In the case of linear classification, a new bounding scheme based on Bernstein inequality has been proposed, which models interval-valued uncertainty in a less conservative fashion and hence is expected to generalize better than the existing methods. Next, potential of partial information such as bounds on second order moments along with support information has been explored. Bounds on second order moments make the resulting classifiers robust to moment estimation errors.
Uncertainty in the dataset will lead to uncertainty in the kernel matrices. A novel distribution free large deviation inequality has been proposed which handles uncertainty in kernels through co-positive programming in a chance constraint setting. Although such formulations are NP hard, under several cases of interest the problem reduces to a convex program. However, the independence assumption mentioned above, is restrictive and may not always define a valid uncertain kernel. To alleviate this problem an affine set based alternative is proposed and using a robust optimization framework the resultant problem is posed as a minimax problem.
In both the cases of Chance Constraint Program or Robust Optimization (for non-linear SVM), mirror descent algorithm (MDA) like procedures have been applied.
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Wang, Chea-Wei, and 王麒瑋. "Index of Kernel Functions for Support Vector Machine." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/43487331964499753219.
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碩士國立成功大學
工業管理科學系碩博士班
91
A Support Vector Machine (SVM) is a learning machine of novel type, based on statistical learning framework. It has become an increasingly popular tool for machine learning tasks such as classification, regression or novelty detection. To increase learning accuracy of a SVM, kernel plays an important role. This research aims at finding an index of kernels for support vector machines. Used simulation data are produced following Dirichlet and normal distributions. A real experiment for choosing kernel functions is also provided.
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Ruei-YaoHuang and 黃瑞堯. "Video and Image Applications Based on Kernel Support Vector Machine (SVM)." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/67090107280042729427.
Full textAbstract:
碩士國立成功大學
電機工程學系碩博士班
98
We use a classification method based on Kernel support vector machines (Kernel SVM), that can be applied to various types of data. We use Kernel SVM to extract the video highlights of sport and classify textile grade. Different form original classification method, we optimize the parameters and the features by Genetic Algorithm. The Kernel SVM is composed of the training mode and the analysis mode. In the training mode, we adopt the Kernel SVM to train classification function. In the analysis mode, we use the classification function to generate the classification result. We use the video and audio features without predefining any highlight rule of the events. The precision of highlight extraction by Kernel SVM can achieve about 81%, while that of textile grade classification is approximately 83% The experimental results show the proposed method can extract video highlights of sport, and it can also be applied to textile grade classification.
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"Image representation, processing and analysis by support vector regression." 2001. http://library.cuhk.edu.hk/record=b5890679.
Full textAbstract:
Chow Kai Tik = 支援矢量回歸法之影像表示式及其影像處理與分析 / 周啓迪.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.
Includes bibliographical references (leaves 380-383).
Text in English; abstracts in English and Chinese.
Chow Kai Tik = Zhi yuan shi liang hui gui fa zhi ying xiang biao shi shi ji qi ying xiang chu li yu fen xi / Zhou Qidi.
Abstract in English
Abstract in Chinese
Acknowledgement
Content
List of figures
Chapter Chapter 1 --- Introduction --- p.1-11
Chapter 1.1 --- Introduction --- p.2
Chapter 1.2 --- Road Map --- p.9
Chapter Chapter 2 --- Review of Support Vector Machine --- p.12-124
Chapter 2.1 --- Structural Risk Minimization (SRM) --- p.13
Chapter 2.1.1 --- Introduction
Chapter 2.1.2 --- Structural Risk Minimization
Chapter 2.2 --- Review of Support Vector Machine --- p.21
Chapter 2.2.1 --- Review of Support Vector Classification
Chapter 2.2.2 --- Review of Support Vector Regression
Chapter 2.2.3 --- Review of Support Vector Clustering
Chapter 2.2.4 --- Summary of Support Vector Machines
Chapter 2.3 --- Implementation of Support Vector Machines --- p.60
Chapter 2.3.1 --- Kernel Adatron for Support Vector Classification (KA-SVC)
Chapter 2.3.2 --- Kernel Adatron for Support Vector Regression (KA-SVR)
Chapter 2.3.3 --- Sequential Minimal Optimization for Support Vector Classification (SMO-SVC)
Chapter 2.3.4 --- Sequential Minimal Optimization for Support Vector Regression (SMO-SVR)
Chapter 2.3.5 --- Lagrangian Support Vector Classification (LSVC)
Chapter 2.3.6 --- Lagrangian Support Vector Regression (LSVR)
Chapter 2.4 --- Applications of Support Vector Machines --- p.117
Chapter 2.4.1 --- Applications of Support Vector Classification
Chapter 2.4.2 --- Applications of Support Vector Regression
Chapter Chapter 3 --- Image Representation by Support Vector Regression --- p.125-183
Chapter 3.1 --- Introduction of SVR Representation --- p.116
Chapter 3.1.1 --- Image Representation by SVR
Chapter 3.1.2 --- Implicit Smoothing of SVR representation
Chapter 3.1.3 --- "Different Insensitivity, C value, Kernel and Kernel Parameters"
Chapter 3.2 --- Variation on Encoding Method [Training Process] --- p.154
Chapter 3.2.1 --- Training SVR with Missing Data
Chapter 3.2.2 --- Training SVR with Image Blocks
Chapter 3.2.3 --- Training SVR with Other Variations
Chapter 3.3 --- Variation on Decoding Method [Testing pr Reconstruction Process] --- p.171
Chapter 3.3.1 --- Reconstruction with Different Portion of Support Vectors
Chapter 3.3.2 --- Reconstruction with Different Support Vector Locations and Lagrange Multiplier Values
Chapter 3.3.3 --- Reconstruction with Different Kernels
Chapter 3.4 --- Feature Extraction --- p.177
Chapter 3.4.1 --- Features on Simple Shape
Chapter 3.4.2 --- Invariant of Support Vector Features
Chapter Chapter 4 --- Mathematical and Physical Properties of SYR Representation --- p.184-243
Chapter 4.1 --- Introduction of RBF Kernel --- p.185
Chapter 4.2 --- Mathematical Properties: Integral Properties --- p.187
Chapter 4.2.1 --- Integration of an SVR Image
Chapter 4.2.2 --- Fourier Transform of SVR Image (Hankel Transform of Kernel)
Chapter 4.2.3 --- Cross Correlation between SVR Images
Chapter 4.2.4 --- Convolution of SVR Images
Chapter 4.3 --- Mathematical Properties: Differential Properties --- p.219
Chapter 4.3.1 --- Review of Differential Geometry
Chapter 4.3.2 --- Gradient of SVR Image
Chapter 4.3.3 --- Laplacian of SVR Image
Chapter 4.4 --- Physical Properties --- p.228
Chapter 4.4.1 --- 7Transformation between Reconstructed Image and Lagrange Multipliers
Chapter 4.4.2 --- Relation between Original Image and SVR Approximation
Chapter 4.5 --- Appendix --- p.234
Chapter 4.5.1 --- Hankel Transform for Common Functions
Chapter 4.5.2 --- Hankel Transform for RBF
Chapter 4.5.3 --- Integration of Gaussian
Chapter 4.5.4 --- Chain Rules for Differential Geometry
Chapter 4.5.5 --- Derivation of Gradient of RBF
Chapter 4.5.6 --- Derivation of Laplacian of RBF
Chapter Chapter 5 --- Image Processing in SVR Representation --- p.244-293
Chapter 5.1 --- Introduction --- p.245
Chapter 5.2 --- Geometric Transformation --- p.241
Chapter 5.2.1 --- "Brightness, Contrast and Image Addition"
Chapter 5.2.2 --- Interpolation or Resampling
Chapter 5.2.3 --- Translation and Rotation
Chapter 5.2.4 --- Affine Transformation
Chapter 5.2.5 --- Transformation with Given Optical Flow
Chapter 5.2.6 --- A Brief Summary
Chapter 5.3 --- SVR Image Filtering --- p.261
Chapter 5.3.1 --- Discrete Filtering in SVR Representation
Chapter 5.3.2 --- Continuous Filtering in SVR Representation
Chapter Chapter 6 --- Image Analysis in SVR Representation --- p.294-370
Chapter 6.1 --- Contour Extraction --- p.295
Chapter 6.1.1 --- Contour Tracing by Equi-potential Line [using Gradient]
Chapter 6.1.2 --- Contour Smoothing and Contour Feature Extraction
Chapter 6.2 --- Registration --- p.304
Chapter 6.2.1 --- Registration using Cross Correlation
Chapter 6.2.2 --- Registration using Phase Correlation [Phase Shift in Fourier Transform]
Chapter 6.2.3 --- Analysis of the Two Methods for Registrationin SVR Domain
Chapter 6.3 --- Segmentation --- p.347
Chapter 6.3.1 --- Segmentation by Contour Tracing
Chapter 6.3.2 --- Segmentation by Thresholding on Smoothed or Sharpened SVR Image
Chapter 6.3.3 --- Segmentation by Thresholding on SVR Approximation
Chapter 6.4 --- Appendix --- p.368
Chapter Chapter 7 --- Conclusion --- p.371-379
Chapter 7.1 --- Conclusion and contribution --- p.372
Chapter 7.2 --- Future work --- p.378
Reference --- p.380-383
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Asharaf, S. "Efficient Kernel Methods For Large Scale Classification." Thesis, 2007. http://hdl.handle.net/2005/1076.
Full textAbstract:
Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms that scale well with the size of the data sets involved. A classification algorithm is said to be scalable if there is no significant increase in time and space requirements for the algorithm (without compromising the generalization performance) when dealing with an increase in the training set size. Support Vector Machine (SVM) is one of the most celebrated kernel based classification methods used in Machine Learning. An SVM capable of handling large scale classification problems will definitely be an ideal candidate in many real world applications. The training process involved in SVM classifier is usually formulated as a Quadratic Programing(QP) problem. The existing solution strategies for this problem have an associated time and space complexity that is (at least) quadratic in the number of training points. This makes the SVM training very expensive even on classification problems having a few thousands of training examples.
This thesis addresses the scalability of the training algorithms involved in both two class and multiclass Support Vector Machines. Efficient training schemes reducing the space and time requirements of the SVM training process are proposed as possible solutions. The classification schemes discussed in the thesis for handling large scale two class classification problems are a) Two selective sampling based training schemes for scaling Non-linear SVM and b) Clustering based approaches for handling unbalanced data sets with Core Vector Machine. To handle large scale multicalss classification problems, the thesis proposes Multiclass Core Vector Machine (MCVM), a scalable SVM based multiclass classifier. In MVCM, the multiclass SVM problem is shown to be equivalent to a Minimum Enclosing Ball (MEB) problem and is then solved using a fast approximate MEB finding algorithm. Experimental studies were done with several large real world data sets such as IJCNN1 and Acoustic data sets from LIBSVM page, Extended USPS data set from CVM page and network intrusion detection data sets of DARPA, US Defense used in KDD 99 contest. From the empirical results it is observed that the proposed classification schemes achieve good generalization performance at low time and space requirements. Further, the scalability experiments done with large training data sets have demonstrated that the proposed schemes scale well. A novel soft clustering scheme called Rough Support Vector Clustering (RSVC) employing the idea of Soft Minimum Enclosing Ball Problem (SMEB) is another contribution discussed in this thesis. Experiments done with a synthetic data set and the real world data set namely IRIS, have shown that RSVC finds meaningful soft cluster abstractions.
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Miao, Chuxiong. "A support vector machine model for pipe crack size classification." Master's thesis, 2009. http://hdl.handle.net/10048/400.
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Thesis (M. Sc.)--University of Alberta, 2009.Title from pdf file main screen (viewed on July 16, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Mechanical Engineering, University of Alberta." Includes bibliographical references.
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"Sparse learning under regularization framework." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075111.
Full textAbstract:
Regularization is a dominant theme in machine learning and statistics due to its prominent ability in providing an intuitive and principled tool for learning from high-dimensional data. As large-scale learning applications become popular, developing efficient algorithms and parsimonious models become promising and necessary for these applications. Aiming at solving large-scale learning problems, this thesis tackles the key research problems ranging from feature selection to learning with unlabeled data and learning data similarity representation. More specifically, we focus on the problems in three areas: online learning, semi-supervised learning, and multiple kernel learning.The first part of this thesis develops a novel online learning framework to solve group lasso and multi-task feature selection. To the best our knowledge, the proposed online learning framework is the first framework for the corresponding models. The main advantages of the online learning algorithms are that (1) they can work on the applications where training data appear sequentially; consequently, the training procedure can be started at any time; (2) they can handle data up to any size with any number of features. The efficiency of the algorithms is attained because we derive closed-form solutions to update the weights of the corresponding models. At each iteration, the online learning algorithms just need O (d) time complexity and memory cost for group lasso, while they need O (d x Q) for multi-task feature selection, where d is the number of dimensions and Q is the number of tasks. Moreover, we provide theoretical analysis for the average regret of the online learning algorithms, which also guarantees the convergence rate of the algorithms. In addition, we extend the online learning framework to solve several related models which yield more sparse solutions.
The second part of this thesis addresses a general scenario of semi-supervised learning for the binary classification problern, where the unlabeled data may be a mixture of relevant and irrelevant data to the target binary classification task. Without specifying the relatedness in the unlabeled data, we develop a novel maximum margin classifier, named the tri-class support vector machine (3C-SVM), to seek an inductive rule that can separate these data into three categories: --1, +1, or 0. This is achieved by adopting a novel min loss function and following the maximum entropy principle. For the implementation, we approximate the problem and solve it by a standard concaveconvex procedure (CCCP). The approach is very efficient and it is possible to solve large-scale datasets.
The third part of this thesis focuses on multiple kernel learning (MKL) to solve the insufficiency of the L1-MKL and the Lp-MKL models. Hence, we propose a generalized MKL (GMKL) model by introducing an elastic net-type constraint on the kernel weights. More specifically, it is an MKL model with a constraint on a linear combination of the L1-norm and the square of the L2-norm on the kernel weights to seek the optimal kernel combination weights. Therefore, previous MKL problems based on the L1-norm or the L2-norm constraints can be regarded as its special cases. Moreover, our GMKL enjoys the favorable sparsity property on the solution and also facilitates the grouping effect. In addition, the optimization of our GMKL is a convex optimization problem, where a local solution is the globally optimal solution. We further derive the level method to efficiently solve the optimization problem.
Yang, Haiqin.
Advisers: Kuo Chin Irwin King; Michael Rung Tsong Iyu.
Source: Dissertation Abstracts International, Volume: 73-04, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (leaves 152-173).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
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Baek, Seung Hyun. "Kernel-Based Data Mining Approach with Variable Selection for Nonlinear High-Dimensional Data." 2010. http://trace.tennessee.edu/utk_graddiss/676.
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In statistical data mining research, datasets often have nonlinearity and high-dimensionality. It has become difficult to analyze such datasets in a comprehensive manner using traditional statistical methodologies. Kernel-based data mining is one of the most effective statistical methodologies to investigate a variety of problems in areas including pattern recognition, machine learning, bioinformatics, chemometrics, and statistics. In particular, statistically-sophisticated procedures that emphasize the reliability of results and computational efficiency are required for the analysis of high-dimensional data. In this dissertation, first, a novel wrapper method called SVM-ICOMP-RFE based on hybridized support vector machine (SVM) and recursive feature elimination (RFE) with information-theoretic measure of complexity (ICOMP) is introduced and developed to classify high-dimensional data sets and to carry out subset selection of the variables in the original data space for finding the best for discriminating between groups. Recursive feature elimination (RFE) ranks variables based on the information-theoretic measure of complexity (ICOMP) criterion. Second, a dual variables functional support vector machine approach is proposed. The proposed approach uses both the first and second derivatives of the degradation profiles. The modified floating search algorithm for the repeated variable selection, with newly-added degradation path points, is presented to find a few good variables while reducing the computation time for on-line implementation. Third, a two-stage scheme for the classification of near infrared (NIR) spectral data is proposed. In the first stage, the proposed multi-scale vertical energy thresholding (MSVET) procedure is used to reduce the dimension of the high-dimensional spectral data. In the second stage, a few important wavelet coefficients are selected using the proposed SVM gradient-recursive feature elimination (RFE). Fourth, a novel methodology based on a human decision making process for discriminant analysis called PDCM is proposed. The proposed methodology consists of three basic steps emulating the thinking process: perception, decision, and cognition. In these steps two concepts known as support vector machines for classification and information complexity are integrated to evaluate learning models.
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Sentelle, Christopher. "Practical Implementations of the Active Set Method for Support Vector Machine Training with Semi-definite Kernels." Doctoral diss., 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6178.
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The Support Vector Machine (SVM) is a popular binary classification model due to its superior generalization performance, relative ease-of-use, and applicability of kernel methods. SVM training entails solving an associated quadratic programming (QP) that presents significant challenges in terms of speed and memory constraints for very large datasets; therefore, research on numerical optimization techniques tailored to SVM training is vast. Slow training times are especially of concern when one considers that re-training is often necessary at several values of the model's regularization parameter, C, as well as associated kernel parameters.
The active set method is suitable for solving SVM problem and is in general ideal when the Hessian is dense and the solution is sparse-the case for the l1-loss SVM formulation. There has recently been renewed interest in the active set method as a technique for exploring the entire SVM regularization path, which has been shown to solve the SVM solution at all points along the regularization path (all values of C) in not much more time than it takes, on average, to perform training at a single value of C with traditional methods. Unfortunately, the majority of active set implementations used for SVM training require positive definite kernels, and those implementations that do allow semi-definite kernels tend to be complex and can exhibit instability and, worse, lack of convergence. This severely limits applicability since it precludes the use of the linear kernel, can be an issue when duplicate data points exist, and doesn't allow use of low-rank kernel approximations to improve tractability for large datasets.
The difficulty, in the case of a semi-definite kernel, arises when a particular active set results in a singular KKT matrix (or the equality-constrained problem formed using the active set is semi-definite). Typically this is handled by explicitly detecting the rank of the KKT matrix. Unfortunately, this adds significant complexity to the implementation; and, if care is not taken, numerical instability, or worse, failure to converge can result. This research shows that the singular KKT system can be avoided altogether with simple modifications to the active set method. The result is a practical, easy to implement active set method that does not need to explicitly detect the rank of the KKT matrix nor modify factorization or solution methods based upon the rank.
Methods are given for both conventional SVM training as well as for computing the regularization path that are simple and numerically stable. First, an efficient revised simplex method is efficiently implemented for SVM training (SVM-RSQP) with semi-definite kernels and shown to out-perform competing active set implementations for SVM training in terms of training time as well as shown to perform on-par with state-of-the-art SVM training algorithms such as SMO and SVMLight. Next, a new regularization path-following algorithm for semi-definite kernels (Simple SVMPath) is shown to be orders of magnitude faster, more accurate, and significantly less complex than competing methods and does not require the use of external solvers. Theoretical analysis reveals new insights into the nature of the path-following algorithms. Finally, a method is given for computing the approximate regularization path and approximate kernel path using the warm-start capability of the proposed revised simplex method (SVM-RSQP) and shown to provide significant, orders of magnitude, speed-ups relative to the traditional “grid search” where re-training is performed at each parameter value. Surprisingly, it also shown that even when the solution for the entire path is not desired, computing the approximate path can be seen as a speed-up mechanism for obtaining the solution at a single value. New insights are given concerning the limiting behaviors of the regularization and kernel path as well as the use of low-rank kernel approximations.Ph.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering
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Phoungphol, Piyaphol. "A Classification Framework for Imbalanced Data." 2013. http://scholarworks.gsu.edu/cs_diss/78.
Full textAbstract:
As information technology advances, the demands for developing a reliable and highly accurate predictive model from many domains are increasing. Traditional classification algorithms can be limited in their performance on highly imbalanced data sets. In this dissertation, we study two common problems when training data is imbalanced, and propose effective algorithms to solve them.
Firstly, we investigate the problem in building a multi-class classification model from imbalanced class distribution. We develop an effective technique to improve the performance of the model by formulating the problem as a multi-class SVM with an objective to maximize G-mean value. A ramp loss function is used to simplify and solve the problem. Experimental results on multiple real-world datasets confirm that our new method can effectively solve the multi-class classification problem when the datasets are highly imbalanced.
Secondly, we explore the problem in learning a global classification model from distributed data sources with privacy constraints. In this problem, not only data sources have different class distributions but combining data into one central data is also prohibited. We propose a privacy-preserving framework for building a global SVM from distributed data sources. Our new framework avoid constructing a global kernel matrix by mapping non-linear inputs to a linear feature space and then solve a distributed linear SVM from these virtual points. Our method can solve both imbalance and privacy problems while achieving the same level of accuracy as regular SVM.
Finally, we extend our framework to handle high-dimensional data by utilizing Generalized Multiple Kernel Learning to select a sparse combination of features and kernels. This new model produces a smaller set of features, but yields much higher accuracy.
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Evgeniou, Theodoros, and Massimiliano Pontil. "A Note on the Generalization Performance of Kernel Classifiers with Margin." 2000. http://hdl.handle.net/1721.1/7169.
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We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived.
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Juozenaite, Ineta. "Application of machine learning techniques for solving real world business problems : the case study - target marketing of insurance policies." Master's thesis, 2018. http://hdl.handle.net/10362/32410.
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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe concept of machine learning has been around for decades, but now it is becoming more and more popular not only in the business, but everywhere else as well. It is because of increased amount of data, cheaper data storage, more powerful and affordable computational processing. The complexity of business environment leads companies to use data-driven decision making to work more efficiently. The most common machine learning methods, like Logistic Regression, Decision Tree, Artificial Neural Network and Support Vector Machine, with their applications are reviewed in this work. Insurance industry has one of the most competitive business environment and as a result, the use of machine learning techniques is growing in this industry. In this work, above mentioned machine learning methods are used to build predictive model for target marketing campaign of caravan insurance policies to achieve greater profitability. Information Gain and Chi-squared metrics, Regression Stepwise, R package “Boruta”, Spearman correlation analysis, distribution graphs by target variable, as well as basic statistics of all variables are used for feature selection. To solve this real-world business problem, the best final chosen predictive model is Multilayer Perceptron with backpropagation learning algorithm with 1 hidden layer and 12 hidden neurons.