Relevant bibliographies by topics / Support vector machine. Interval. Kernel

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Support vector machine. Interval. Kernel'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Support vector machine. Interval. Kernel"

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Gao, Hong Bing, Liao Yang, Xian Zhang, and Chen Cheng. "Application and Experimental Study of Support Vector Machine in Rolling Bearing Fault." Applied Mechanics and Materials 48-49 (February 2011): 241–45. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.241.

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A brief introduction of the basic concepts of the classification interval, the optimal classification surface and support vector; explained derivation of SVM based on Lagrange optimization method; Sigmoid kernel function and so on. It describes three methods of C-SVM、V-SVM and least squares SVM based on Sigmoid kernel function. To a bearing failure as a example to compare three results of SVM training of the kernel linear function, polynomial kernel function, Sigmoid kernel function, The results show that satisfactory fault analysis demand the appropriate kernel function selection. Fault in the gear box, the bearing failure is 19%, In addition, the rate is as high as 30% in other rotating machinery system failure [1,2].Thus, rolling bearing condition monitoring and fault diagnosis are very important to production safety, and many scholars have done numerous studies [3,4]. Support vector machine method is a learning methods based on statistical learning theory Vapnik-Chervonenkis dimension theory and structural risk minimization [5,6].
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Wu, Chong, Lu Wang, and Zhe Shi. "Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function." Journal of Intelligent Systems 25, no. 3 (July 1, 2016): 417–29. http://dx.doi.org/10.1515/jisys-2014-0132.

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AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.
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Jain, Paras, CH N. V. S. Praneeth, Iragavarapu Kannan, Potluri Harsha Sai, and Jaba Deva Krupa Abel. "Electrocardiogram Beat Classification Using Data Filtration Technique and Support Vector Machine." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3613–20. http://dx.doi.org/10.1166/jctn.2020.9240.

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This work addresses the automatic classification of arrhythmia beats into four generalized classes as described by the Association for the Advancement of Medical Instrumentation (AAMI) standard. We propose a method that includes time-series, statistical and frequency features of RR-interval, DWT, and EMD analysis of QRS morphology. Also, a data filtration technique using support vector selection and under-sampling is applied to find those features as well as data points having significant prediction capabilities. While testing the above combination on MIT-BIH arrhythmia database, adopting the inter-patient paradigm, we achieved 70%, 99.79%, 64.5%, and 80.55% Se and 61.76%, 94.64%, 83.22%, and 77.48% PPV for F, N, SVEB, and VEB classes respectively. Further, the proposed method reduced the classifier’s complexity through feature selection and computation time by data reduction while maintaining the generalization capability of the model. Another finding includes the significant contribution that RR-interval, 180–360 Hz and 0–45 Hz band power, and non-linear statistical characteristics have in distinguishing the arrhythmia classes. The feature and data selection criterion used is F -score and one-class classification by RBF-SVM respectively. The classifier used for building the final model is SVM with the cubic kernel.
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Audina, Nur, Vincentius P. Siregar, and I. Wayan Nurjaya. "ANALISIS PERUBAHAN SEBARAN MANGROVE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DENGAN CITRA LANDSAT DI KABUPATEN BINTAN KEPULAUAN RIAU." Jurnal Ilmu dan Teknologi Kelautan Tropis 11, no. 1 (April 1, 2019): 49–63. http://dx.doi.org/10.29244/jitkt.v11i1.22468.

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ABSTRAKMangrove berfungsi sebagai pelindung abrasi pantai, kawasan pemijahan serta sebagai habitat alami bagi biota darat dan laut. Mangrove banyak dimanfaatkan sebagai penghasil kayu, kawasan wisata serta wilayah konservasi. Adanya pemanfaatan mangrove tersebut menyebabkan terjadi perubahan luasan mangrove yang akan berdampak pada keseimbangan ekosistem perairan. Penelitian ini bertujuan untuk menganalisis perubahan luasan mangrove menggunakan citra satelit Landsat dengan interval waktu 4 tahun (2005 - 2017). Data yang digunakan adalah citra satelit Landsat 5 (2005, 2009) dan Landsat 8 (2013, 2017) pada 3 lokasi yaitu (Desa Berakit, Bintan Buyu dan Teluk Sesah). Algoritma yang digunakan dalam tahap klasifikasi adalah Maximum Likelihood (MLH) dan Support Vector Machine (SVM) dengan 4 kernel. Perubahan penutup lahan selanjutnya dianalisis berkaitan dengan sebaran muatan padatan tersuspensi (MPT). Hasil penelitian menunjukkan mangrove, pemukiman dan perkebunan mengalami pertambahan luasan pada 3 desa tersebut. Hasil klasifikasi tutupan lahan menunjukkan algoritma SVM kernel Radial Basis Function (RBF) memberikan akurasi yang tinggi, yaitu 70,42% dengan koefisien kappa 0,61, sedangkan hasil uji signifikansi menunjukkan bahwa SVM dengan kernel RBF tidak memiliki perbedaan yang signifikan dengan kernel Sigmoid. Berdasarkan tahun 2005-2017, adanya perubahan alih fungsi lahan memberikan dampak pada konsentrasi MPT karena memiliki korelasi yang tinggi serta berpengaruh terhadap perubahan garis pantai yaitu abrasi (Berakit) dan akresi (Bintan Buyu dan Teluk Sesah). ABSTRACTMangrove serves as a protector for coastal abrasion, spawning ground, and natural habitats of species of terrestrial and marine biota. It is widely used for producing woods, tourist areas and conservation areas. The change of its functions above will therefore affect to altering its area cover that is impacted to an imbalance of aquatic ecosystems. This study aimed to analyze the changes of mangrove extent using the Landsat images with data acquisition (2005- 2017) with interval 4 years. The data used in this study were Landsat 5 (2005, 2009) and Landsat 8 (2013 and 2017) at 3 villages (Berakit, Bintan Buyu and Teluk Sesah). The data were analyzed by using algorithms of Maximum Likelihood (MLH) and Support Vector Machine (SVM) with 4 kernels. The change of mangrove cover was then analyzed according to Total Suspended Solid (TSS). The results showed that mangroves, settlements and plantations had increase in the 3 villages. The land cover classification showed that SVM algorithm with kernel Radial Basis Function (RBF) gave high accuracy of 70.42% with coefficient kappa 0.61 while significance test showed no significant difference with SVM Sigmoid kernel type. Based on 2005-2017, changes in land use change have an impact on MPT concentration because it has a high correlation and has an effect on shoreline changes namely abrasion (Berakit) and accretion (Bintan Buyu and Teluk Sesah).
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Wirasati, Ilsya, Zuherman Rustam, Jane Eva Aurelia, Sri Hartini, and Glori Stephani Saragih. "Comparison some of kernel functions with support vector machines classifier for thalassemia dataset." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 430. http://dx.doi.org/10.11591/ijai.v10.i2.pp430-437.

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<span id="docs-internal-guid-9a30056f-7fff-8ff1-59e1-69f89f4280bd"><span>In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%. </span></span>
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Liu, Zhi, Shuqiong Xu, Yun Zhang, Xin Chen, and C. L. Philip Chen. "Interval type-2 fuzzy kernel based support vector machine algorithm for scene classification of humanoid robot." Soft Computing 18, no. 3 (July 6, 2013): 589–606. http://dx.doi.org/10.1007/s00500-013-1080-0.

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Lu, Zhengdong, Todd K. Leen, and Jeffrey Kaye. "Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals." Neural Computation 23, no. 9 (September 2011): 2390–420. http://dx.doi.org/10.1162/neco_a_00164.

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We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.
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Trabelsi, Imen, and Med Salim Bouhlel. "Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition." International Journal of Synthetic Emotions 6, no. 2 (July 2015): 57–68. http://dx.doi.org/10.4018/ijse.2015070104.

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Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models (GMM) and Support Vector Machines (SVM). GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters (mean vectors, covariance matrices, and mixture weights). GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval (GUMI) kernel based on the Bhattacharyya distance is successfully used. CFSSubsetEval combined with Best first algorithm and Greedy stepwise were also utilized on the supervectors space in order to select the most important features. This framework is illustrated using Mel-frequency cepstral (MFCC) coefficients and Perceptual Linear Prediction (PLP) features on two different emotional databases namely the Surrey Audio-Expressed Emotion and the Berlin Emotional speech Database.
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PACHORI, RAM BILAS, MOHIT KUMAR, PAKALA AVINASH, KORA SHASHANK, and U. RAJENDRA ACHARYA. "AN IMPROVED ONLINE PARADIGM FOR SCREENING OF DIABETIC PATIENTS USING RR-INTERVAL SIGNALS." Journal of Mechanics in Medicine and Biology 16, no. 01 (February 2016): 1640003. http://dx.doi.org/10.1142/s0219519416400030.

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Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.
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Na, Hyun Seok, and Khae Hawn Kim. "Development of urination recognition technology based on Support Vector Machine using a smart band." Journal of Exercise Rehabilitation 17, no. 4 (August 23, 2021): 287–92. http://dx.doi.org/10.12965/jer.2142474.237.

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The purpose of this study was to explore the feasibility of a urination management system by developing a smart band-based algorithm that recognizes the urination interval of women. We designed a device that recognizes the time and interval of urination based on the patient’s specific posture and posture changes. The technology used for recognition applied the Radial Basis Function kernel-based Support Vector Machine, a teaching and learning method that facilitates multidimensional analysis by simultaneously judging the characteristics of complex learning data. In order to evaluate the performance of the proposed recognition technique, we compared actual urination and device-sensed urination. An experiment was performed to evaluate the performance of the recognition technology proposed in this study. The efficacy of smart band monitoring urination was evaluated in 10 female patients without urination problems. The entire experiment was performed over a total of 3 days. The average age of the participants was 28.73 years (26–34 years), and there were no signs of dysuria. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 91.0%, proving the robustness of the proposed algorithm. This urination behavior recognition technique shows high accuracy and can be applied in clinical settings to characterize urination patterns in female patients. As wearable devices develop and become more common, algorithms that detect specific sequential body movement patterns that reflect specific physiological behaviors could become a new methodology to study human physiological behavior.
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Dissertations / Theses on the topic "Support vector machine. Interval. Kernel"

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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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Made available in DSpace on 2014-12-17T14:55:12Z (GMT). No. of bitstreams: 1 AdrianaT_TESE.pdf: 618602 bytes, checksum: 8ea994949daea03408599ce92036681a (MD5) Previous issue date: 2012-09-26
The 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 main
Nowadays, 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 Science
Ph.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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Books on the topic "Support vector machine. Interval. Kernel"

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missing], [name. Least squares support vector machines. Singapore: World Scientific, 2002.

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J, Smola Alexander, ed. Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, Mass: MIT Press, 2002.

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Lorenzo, Bruzzone, ed. Kernel methods for remote sensing 1: Data analysis 2. Hoboken, NJ: Wiley, 2009.

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Léon-Charles, Tranchevent, Moor Bart, Moreau Yves, and SpringerLink (Online service), eds. Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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(Editor), Bernhard Schölkopf, Christopher J. C. Burges (Editor), and Alexander J. Smola (Editor), eds. Advances in Kernel Methods: Support Vector Learning. The MIT Press, 1998.

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Bernhard, Schölkopf, Burges Christopher J. C, and Smola Alexander J, eds. Advances in kernel methods: Support vector learning. Cambridge, Mass: MIT Press, 1999.

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Least squares support vector machines. River Edge, NJ: World Scientific, 2002.

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Vandewalle, Joos, Bart De Moor, Tony Van Gestel, Jos De Brabanter, and Johan A. K. Suykens. Least Squares Support Vector Machines. World Scientific Publishing Company, 2003.

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Kernel Methods and Machine Learning. Cambridge University Press, 2014.

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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.

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Book chapters on the topic "Support vector machine. Interval. Kernel"

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Wu, Qing, Boyan Zang, Zongxian Qi, and Yue Gao. "Wavelet Kernel Twin Support Vector Machine." In Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications, 765–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03766-6_86.

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Chen, Guangyi, Tien Dai Bui, Adam Krzyzak, and Weihua Liu. "Support Vector Machine with Customized Kernel." In Advances in Neural Networks – ISNN 2013, 258–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39065-4_32.

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Hamdani, Tarek M., and Adel M. Alimi. "β_SVM a new Support Vector Machine kernel." In Artificial Neural Nets and Genetic Algorithms, 63–68. Vienna: Springer Vienna, 2003. http://dx.doi.org/10.1007/978-3-7091-0646-4_13.

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Imam, Tasadduq, and Kevin Tickle. "Class Information Adapted Kernel for Support Vector Machine." In Lecture Notes in Computer Science, 116–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17534-3_15.

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Liu, Lijuan, Bo Shen, and Xing Wang. "Research on Kernel Function of Support Vector Machine." In Lecture Notes in Electrical Engineering, 827–34. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7262-5_93.

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Chen, Degang, Qiang He, and Xizhao Wang. "The Infinite Polynomial Kernel for Support Vector Machine." In Advanced Data Mining and Applications, 267–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_32.

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Wu, Xia, Wanmei Tang, and Xiao Wu. "Support Vector Machine Based on Hybrid Kernel Function." In Lecture Notes in Electrical Engineering, 127–33. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_17.

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Onodera, Taku, and Tetsuo Shibuya. "The Gapped Spectrum Kernel for Support Vector Machines." In Machine Learning and Data Mining in Pattern Recognition, 1–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39712-7_1.

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Padierna, Luis Carlos, Juan Martín Carpio, María del Rosario Baltazar, Héctor José Puga, and Héctor Joaquín Fraire. "Multiple Kernel Support Vector Machine Problem Is NP-Complete." In Nature-Inspired Computation and Machine Learning, 152–59. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13650-9_14.

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Nguyen, XuanLong, Ling Huang, and Anthony D. Joseph. "Support Vector Machines, Data Reduction, and Approximate Kernel Matrices." In Machine Learning and Knowledge Discovery in Databases, 137–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87481-2_10.

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Conference papers on the topic "Support vector machine. Interval. Kernel"

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Takahashi, Adriana, Adriao D. Doria Neto, and Benjamin R. C. Bedregal. "An introduction interval kernel-Based methods applied on Support Vector Machines." In 2012 8th International Conference on Natural Computation (ICNC). IEEE, 2012. http://dx.doi.org/10.1109/icnc.2012.6234756.

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Li, Yuejiao, Weiguo Zeng, Xiufeng Li, Fajun Ren, and Haijun Hu. "Rank Predictions of Internal Corrosion of Gathering Pipelines in a Natural Gas Field With a Multi-Kernel SVM Method." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21333.

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Abstract Internal CO2/H2S corrosion of gathering pipelines is a serious problem in natural gas plant. It is important for field engineers to assess the corrosion degree and control corrosion risk. A multi-kernel support-vector-machine (SVM) method is presented to rank internal corrosion of gathering pipelines according to the NACE RP-0775-91 standard. By considering the nonlinear indivisibility between data, we combined three kinds of kernels (linear kernel, polynomial kernel, and Gaussian kernel) into a multi-kernel SVM to rank the internal CO2/H2S corrosion of gathering pipelines. The method was applied to a natural gas field in northwest China. Corrosion data were collected and analyzed. The prediction accuracy of the multi-kernel SVM method for ranking CO2/H2S corrosion was 66%, which is higher than the results of the single-kernel SVM methods (linear kernel, polynomial kernel and Gaussian kernel), whose prediction accuracies are 50%, 48% and 54% respectively. These findings could help field engineers rank corrosion and reduce the corrosion risk.
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Klomjit, J., S. Thongsuk, and Atthapol Ngaopitakkul. "Selection of Proper Non-linear Kernel Parameter in Support Vector Machine Algorithm for Classifying the Internal Fault Type in Winding Power Transformer." In The 2nd International Conference on Intelligent Systems and Image Processing 2014. The Institute of Industrial Applications Engineers, 2014. http://dx.doi.org/10.12792/icisip2014.072.

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Wulandari, Iffandya Popy, and Min-Chun Pan. "Internal Resistance Based Assessment Model for the Degradation of Li-Ion Battery Pack." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24502.

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Abstract As one pioneer means for energy storage, Li-ion battery packs have a complex and critical issue about degradation monitoring and remaining useful life estimation. It induces challenges on condition characterization of Li-ion battery packs such as internal resistance (IR). The IR is an essential parameter of a Li-ion battery pack, relating to the energy efficiency, power performance, degradation, and physical life of the li-ion battery pack. This study aims to obtain reliable IR through applying an evaluation test that acquires data such as voltage, current, and temperature provided by the battery management system (BMS). Additionally, this paper proposes an approach to predict the degradation of Li-ion battery pack using support vector regression (SVR) with RBF kernel. The modeling approach using the relationship between internal resistance, different SOC levels 20%–100%, and cycle at the beginning of life 1 cycle until cycle 500. The data-driven method is used here to achieve battery life prediction.based on internal resistance behavior in every period using supervised machine learning, SVR. Our experiment result shows that the internal resistance was increasing non-linear, approximately 0.24%, and it happened if the cycle rise until 500 cycles. Besides, using SVR algorithm, the quality of the fitting was evaluated using coefficient determination R2, and the score is 0.96. In the proposed modeling process of the battery pack, the value of MSE is 0.000035.
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Zhi-Peng Xie, Duan-Sheng Chen, Song-Can Chen, Li-Shan Qiao, and Bo Yang. "A tight support kernel for support vector machine." In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635824.

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Cheng, Gong, and Xiaojun Tong. "Fuzzy Clustering Multiple Kernel Support Vector Machine." In 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2018. http://dx.doi.org/10.1109/icwapr.2018.8521307.

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Pan, Zhi-Bin, Hong Chen, and Xin-Hua You. "Support vector machine with orthogonal Legendre kernel." In 2012 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2012. http://dx.doi.org/10.1109/icwapr.2012.6294766.

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Ye, Ren, and P. N. Suganthan. "A kernel-ensemble bagging support vector machine." In 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2012. http://dx.doi.org/10.1109/isda.2012.6416648.

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Raicharoen, T., and C. Lursinsap. "Critical support vector machine without kernel function." In 9th International Conference on Neural Information Processing. IEEE, 2002. http://dx.doi.org/10.1109/iconip.2002.1201951.

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Ning Ye, Ruixiang Sun, Yingan Liu, and Lin Cao. "Support vector machine with orthogonal Chebyshev kernel." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.1096.

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Reports on the topic "Support vector machine. Interval. Kernel"

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Arun, Ramaiah, and Shanmugasundaram Singaravelan. Classification of Brain Tumour in Magnetic Resonance Images Using Hybrid Kernel Based Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, October 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.

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