Relevant bibliographies by topics / Support vector data description

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

Academic literature on the topic 'Support vector data description'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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Journal articles on the topic "Support vector data description"

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Tax, David M. J., and Robert P. W. Duin. "Support Vector Data Description." Machine Learning 54, no. 1 (2004): 45–66. http://dx.doi.org/10.1023/b:mach.0000008084.60811.49.

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Sadeghi, Reza, and Javad Hamidzadeh. "Automatic support vector data description." Soft Computing 22, no. 1 (2016): 147–58. http://dx.doi.org/10.1007/s00500-016-2317-5.

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Teeyapan, Kasemsit, Nipon Theera-Umpon, and Sansanee Auephanwiriyakul. "Ellipsoidal support vector data description." Neural Computing and Applications 28, S1 (2016): 337–47. http://dx.doi.org/10.1007/s00521-016-2343-3.

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Gorgani, Mohammad Ebrahim, Mahdi Moradi, and Hadi Sadoghi Yazdi. "An Empirical Modeling of Companies Using Support Vector Data Description." International Journal of Trade, Economics and Finance 1, no. 2 (2010): 221–24. http://dx.doi.org/10.7763/ijtef.2010.v1.41.

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WANG, Xiao-ming, Shi-tong WANG, and Hong PENG. "Minimum variance support vector data description." Journal of Computer Applications 32, no. 2 (2013): 416–18. http://dx.doi.org/10.3724/sp.j.1087.2012.00416.

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Rahimzadeh Arashloo, Shervin. "ℓ -Norm Support Vector Data Description". Pattern Recognition 132 (грудень 2022): 108930. http://dx.doi.org/10.1016/j.patcog.2022.108930.

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Sohrab, Fahad, Jenni Raitoharju, Alexandros Iosifidis, and Moncef Gabbouj. "Ellipsoidal Subspace Support Vector Data Description." IEEE Access 8 (2020): 122013–25. http://dx.doi.org/10.1109/access.2020.3007123.

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Huang, Guangxin, Huafu Chen, Zhongli Zhou, Feng Yin, and Ke Guo. "Two-class support vector data description." Pattern Recognition 44, no. 2 (2011): 320–29. http://dx.doi.org/10.1016/j.patcog.2010.08.025.

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Sohrab, Fahad, Jenni Raitoharju, Alexandros Iosifidis, and Moncef Gabbouj. "Multimodal subspace support vector data description." Pattern Recognition 110 (February 2021): 107648. http://dx.doi.org/10.1016/j.patcog.2020.107648.

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Cha, Myungraee, Jun Seok Kim, and Jun-Geol Baek. "Density weighted support vector data description." Expert Systems with Applications 41, no. 7 (2014): 3343–50. http://dx.doi.org/10.1016/j.eswa.2013.11.025.

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Dissertations / Theses on the topic "Support vector data description"

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Chu, Shun-Kwong. "Scaling up support vector data description by using core-sets /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20CHU.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 60-64). Also available in electronic version. Access restricted to campus users.
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Sfikas, Giorgos. "Modèles statistiques non linéaires pour l'analyse de formes : application à l'imagerie cérébrale." Phd thesis, Université de Strasbourg, 2012. http://tel.archives-ouvertes.fr/tel-00789793.

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Cette thèse a pour objet l'analyse statistique de formes, dans le contexte de l'imagerie médicale.Dans le champ de l'imagerie médicale, l'analyse de formes est utilisée pour décrire la variabilité morphologique de divers organes et tissus. Nous nous focalisons dans cette thèse sur la construction d'un modèle génératif et discriminatif, compact et non-linéaire, adapté à la représentation de formes.Ce modèle est évalué dans le contexte de l'étude d'une population de patients atteints de la maladie d'Alzheimer et d'une population de sujets contrôles sains. Notre intérêt principal ici est l'utilisationdu modèle discriminatif pour découvrir les différences morphologiques les plus discriminatives entre une classe de formes donnée et des formes n'appartenant pas à cette classe. L'innovation théorique apportée par notre modèle réside en deux points principaux : premièrement, nous proposons un outil pour extraire la différence discriminative dans le cadre Support Vector Data Description (SVDD) ; deuxièmement, toutes les reconstructions générées sont anatomiquementcorrectes. Ce dernier point est dû au caractère non-linéaire et compact du modèle, lié à l'hypothèse que les données (les formes) se trouvent sur une variété non-linéaire de dimension faible. Une application de notre modèle à des données médicales réelles montre des résultats cohérents avec les connaissances médicales.
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El, Azami Meriem. "Computer aided diagnosis of epilepsy lesions based on multivariate and multimodality data analysis." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI087/document.

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Environ 150.000 personnes souffrent en France d'une épilepsie partielle réfractaire à tous les médicaments. La chirurgie, qui constitue aujourd’hui le meilleur recours thérapeutique nécessite un bilan préopératoire complexe. L'analyse de données d'imagerie telles que l’imagerie par résonance magnétique (IRM) anatomique et la tomographie d’émission de positons (TEP) au FDG (fluorodéoxyglucose) tend à prendre une place croissante dans ce protocole, et pourrait à terme limiter de recourir à l’électroencéphalographie intracérébrale (SEEG), procédure très invasive mais qui constitue encore la technique de référence. Pour assister les cliniciens dans leur tâche diagnostique, nous avons développé un système d'aide au diagnostic (CAD) reposant sur l'analyse multivariée de données d'imagerie. Compte tenu de la difficulté relative à la constitution de bases de données annotées et équilibrées entre classes, notre première contribution a été de placer l'étude dans le cadre méthodologique de la détection du changement. L'algorithme du séparateur à vaste marge adapté à ce cadre là (OC-SVM) a été utilisé pour apprendre, à partir de cartes multi-paramétriques extraites d'IRM T1 de sujets normaux, un modèle prédictif caractérisant la normalité à l'échelle du voxel. Le modèle permet ensuite de faire ressortir, dans les images de patients, les zones cérébrales suspectes s'écartant de cette normalité. Les performances du système ont été évaluées sur des lésions simulées ainsi que sur une base de données de patients. Trois extensions ont ensuite été proposées. D'abord un nouveau schéma de détection plus robuste à la présence de bruit d'étiquetage dans la base de données d'apprentissage. Ensuite, une stratégie de fusion optimale permettant la combinaison de plusieurs classifieurs OC-SVM associés chacun à une séquence IRM. Enfin, une généralisation de l'algorithme de détection d'anomalies permettant la conversion de la sortie du CAD en probabilité, offrant ainsi une meilleure interprétation de la sortie du système et son intégration dans le bilan pré-opératoire global<br>One third of patients suffering from epilepsy are resistant to medication. For these patients, surgical removal of the epileptogenic zone offers the possibility of a cure. Surgery success relies heavily on the accurate localization of the epileptogenic zone. The analysis of neuroimaging data such as magnetic resonance imaging (MRI) and positron emission tomography (PET) is increasingly used in the pre-surgical work-up of patients and may offer an alternative to the invasive reference of Stereo-electro-encephalo -graphy (SEEG) monitoring. To assist clinicians in screening these lesions, we developed a computer aided diagnosis system (CAD) based on a multivariate data analysis approach. Our first contribution was to formulate the problem of epileptogenic lesion detection as an outlier detection problem. The main motivation for this formulation was to avoid the dependence on labelled data and the class imbalance inherent to this detection task. The proposed system builds upon the one class support vector machines (OC-SVM) classifier. OC-SVM was trained using features extracted from MRI scans of healthy control subjects, allowing a voxelwise assessment of the deviation of a test subject pattern from the learned patterns. System performance was evaluated using realistic simulations of challenging detection tasks as well as clinical data of patients with intractable epilepsy. The outlier detection framework was further extended to take into account the specificities of neuroimaging data and the detection task at hand. We first proposed a reformulation of the support vector data description (SVDD) method to deal with the presence of uncertain observations in the training data. Second, to handle the multi-parametric nature of neuroimaging data, we proposed an optimal fusion approach for combining multiple base one-class classifiers. Finally, to help with score interpretation, threshold selection and score combination, we proposed to transform the score outputs of the outlier detection algorithm into well calibrated probabilities
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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.<br>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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Mao, Jin, Lisa R. Moore, Carrine E. Blank, et al. "Microbial phenomics information extractor (MicroPIE): a natural language processing tool for the automated acquisition of prokaryotic phenotypic characters from text sources." BIOMED CENTRAL LTD, 2016. http://hdl.handle.net/10150/622562.

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Background: The large-scale analysis of phenomic data (i.e., full phenotypic traits of an organism, such as shape, metabolic substrates, and growth conditions) in microbial bioinformatics has been hampered by the lack of tools to rapidly and accurately extract phenotypic data from existing legacy text in the field of microbiology. To quickly obtain knowledge on the distribution and evolution of microbial traits, an information extraction system needed to be developed to extract phenotypic characters from large numbers of taxonomic descriptions so they can be used as input to existing phylogenetic analysis software packages. Results: We report the development and evaluation of Microbial Phenomics Information Extractor (MicroPIE, version 0.1.0). MicroPIE is a natural language processing application that uses a robust supervised classification algorithm (Support Vector Machine) to identify characters from sentences in prokaryotic taxonomic descriptions, followed by a combination of algorithms applying linguistic rules with groups of known terms to extract characters as well as character states. The input to MicroPIE is a set of taxonomic descriptions (clean text). The output is a taxon-by-character matrix-with taxa in the rows and a set of 42 pre-defined characters (e.g., optimum growth temperature) in the columns. The performance of MicroPIE was evaluated against a gold standard matrix and another student-made matrix. Results show that, compared to the gold standard, MicroPIE extracted 21 characters (50%) with a Relaxed F1 score > 0.80 and 16 characters (38%) with Relaxed F1 scores ranging between 0.50 and 0.80. Inclusion of a character prediction component (SVM) improved the overall performance of MicroPIE, notably the precision. Evaluated against the same gold standard, MicroPIE performed significantly better than the undergraduate students. Conclusion: MicroPIE is a promising new tool for the rapid and efficient extraction of phenotypic character information from prokaryotic taxonomic descriptions. However, further development, including incorporation of ontologies, will be necessary to improve the performance of the extraction for some character types.
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D'Orangeville, Vincent. "Analyse automatique de données par Support Vector Machines non supervisés." Thèse, Université de Sherbrooke, 2012. http://hdl.handle.net/11143/6678.

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Cette dissertation présente un ensemble d'algorithmes visant à en permettre un usage rapide, robuste et automatique des « Support Vector Machines » (SVM) non supervisés dans un contexte d'analyse de données. Les SVM non supervisés se déclinent sous deux types algorithmes prometteurs, le « Support Vector Clustering » (SVC) et le « Support Vector Domain Description » (SVDD), offrant respectivement une solution à deux problèmes importants en analyse de données, soit la recherche de groupements homogènes (« clustering »), ainsi que la reconnaissance d'éléments atypiques (« novelty/abnomaly detection ») à partir d'un ensemble de données. Cette recherche propose des solutions concrètes à trois limitations fondamentales inhérentes à ces deux algorithmes, notamment I) l'absence d'algorithme d'optimisation efficace permettant d'exécuter la phase d'entrainement des SVDD et SVC sur des ensembles de données volumineux dans un délai acceptable, 2) le manque d'efficacité et de robustesse des algorithmes existants de partitionnement des données pour SVC, ainsi que 3) l'absence de stratégies de sélection automatique des hyperparamètres pour SVDD et SVC contrôlant la complexité et la tolérance au bruit des modèles générés. La résolution individuelle des trois limitations mentionnées précédemment constitue les trois axes principaux de cette thèse doctorale, chacun faisant l'objet d'un article scientifique proposant des stratégies et algorithmes permettant un usage rapide, robuste et exempt de paramètres d'entrée des SVDD et SVC sur des ensembles de données arbitraires.
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Perez, Daniel Antonio. "Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34858.

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Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.
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Devine, Jon. "Support Vector Methods for Higher-Level Event Extraction in Point Data." Fogler Library, University of Maine, 2009. http://www.library.umaine.edu/theses/pdf/DevineJ2009.pdf.

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Crampton, Andrew. "Radial basis and support vector machine algorithms for approximating discrete data." Thesis, University of Huddersfield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273723.

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The aim of this thesis is to demonstrate how the versatility of radial basis functions can be used to construct algorithms for approximating discrete sets of scattered data. In many cases, these algorithms have been constructed by blending together existing methods or by extending algorithms that exploit certain properties of a particular basis function to include certain radial functions. In the later chapters, we shall see that methods which currently use radial basis functions can be made more efficient by considering a change to the existing methods of solution. In chapter one we introduce radial basis functions (RBFs) and show how they can be used to construct interpolation and approximation models. We examine the uniqueness properties of the interpolation scheme for two specific functions and review some of the methods currently being used to determine the type of function to use and how to choose the number and location of centres. We describe three methods for choosing centres based on data clustering techniques and compare the accuracy of an approximation using two of these schemes. We show through a numerical example how greater accuracy can be achieved by combining these two schemes intelligently to construct a new, hybrid method. Problems that currently exist, for a particular clustering algorithm, when dealing with domain boundaries and which are not covered in great detail in the literature are highlighted and a new method is proposed. We conclude the chapter with an investigation into point distributions on the sphere. Radial basis functions are increasingly being used as a tool for approximating both discrete data and known functions on the sphere. Much of the current research focuses on constructing optimum point distributions for approximations using spherical harmonics. In this section we compare and evaluate these point distributions for RBF approximations and contrast the accuracy of the spherical harmonics with results obtained using the multiquadric function. In chapter two we develop an algorithm for surface approximation by combining the works of Mason & Bennell [40], and Clenshaw & Hayes [18]. Here, the well known method for constructing tensor products on rectangular grids is combined with an algorithm for approximating data collected along curved paths. The method developed in the literature for separable Chebyshev polynomials is extended to include the Gaussian radial function. Since the centres of the Gaussians can be distinct from the data points, we suggest a method for constructing a suitable set of centres to enable the efficiency of the two methods to be preserved. Possibilities for further efficiency using parallel processing are also discussed. We conclude the chapter by reviewing the Gram-Schmidt method and show how the use of orthogonal functions results in a numerically stable computation for evaluating the model parameters. The local support of the Gaussian function is investigated and the method of Mason & Crampton [41] is explained for constructing orthogonalised Gaussian functions. Chapter three introduces a relatively new topic in data approximation called support vector machines (SVMs). The motivation behind using SVMs for constructing regression models to corrupted data is addressed and the use of RBF kernels to map data into feature space is explained. We show how the regression model is formulated and discuss currently used methods of solution. The flexibility of SVMs to adapt to different types and level of noise is demonstrated through some numerical examples. We make use of the techniques developed in SVM regression to show how the algorithm described in chapter two can be extended. Here we make use of SVMs in the early stages of the algorithm to remove the need for further consideration of noise. We complete the discussion of SVMs by explaining their use in the field of data classification through a simple pattern recognition example.Chapter four focuses on a new approach to the solution of an SVM. The new approach taken is one of constructing an entirely linear objective function. This is achieved by changing the regularisation term. We show, in detail, how the changes made to the existing framework affects the construction of the model. We describe the solution method and explain how advantage can be taken of the new linear structure. To determine the model parameters, we show how the solution, in the form of a simplex tableau, can be found extremely efficiently by recognising certain relationships between variables that allow us to employ Lei's algorithm. Examples that show SVM approximants to noisy data for both curves and surfaces are given together with a comparison between Lei's algorithm and a standard simplex solution method. We finish the section by highlighting the link between support vectors and radial basis function centres. The sparsity produced by the method in the coefficient vector is also discussed. The new linearised approach to constructing SVM regression models is used in a new algorithm developed to construct planar curves that model the path of fault lines in a surface. Part of a detection algorithm proposed by Gutzmer & Iske [33] is used to determine points that lie close to a fault line. The new approach is then to model the fault line by constructing an SVM regression curve. The chapter concludes with some examples and remarks. The thesis concludes with Chapter five in which we summarise the main points discussed and point to possibilities for extending the work presented.
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Andreola, Rafaela. "Support Vector Machines na classificação de imagens hiperespectrais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/17894.

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É de conhecimento geral que, em alguns casos, as classes são espectralmente muito similares e que não é possível separá-las usando dados convencionais em baixa dimensionalidade. Entretanto, estas classes podem ser separáveis com um alto grau de acurácia em espaço de alta dimensão. Por outro lado, classificação de dados em alta dimensionalidade pode se tornar um problema para classificadores paramétricos, como o Máxima Verossimilhança Gaussiana (MVG). Um grande número de variáveis que caracteriza as imagens hiperespectrais resulta em um grande número de parâmetros a serem estimados e, geralmente, tem-se um número limitado de amostras de treinamento disponíveis. Essa condição causa o fenômeno de Hughes que consiste na gradual degradação da acurácia com o aumento da dimensionalidade dos dados. Neste contexto, desperta o interesse a utilização de classificadores não-paramétricos, como é o caso de Support Vector Machines (SVM). Nesta dissertação é analisado o desempenho do classificador SVM quando aplicado a imagens hiperespectrais de sensoriamento remoto. Inicialmente os conceitos teóricos referentes à SVM são revisados e discutidos. Em seguida, uma série de experimentos usando dados AVIRIS são realizados usando diferentes configurações para o classificador. Os dados cobrem uma área de teste da Purdue University e apresenta classes de culturas agrícolas espectralmente muito similares. A acurácia produzida na classificação por diferentes kernels são investigadas em função da dimensionalidade dos dados e comparadas com as obtidas com o classificador MVG. Como SVM é aplicado a um par de classes por vez, desenvolveu-se um classificador multi-estágio estruturado em forma de árvore binária para lidar como problema multi-classe. Em cada nó, a seleção do par de classes mais separáveis é feita pelo critério distância de Bhattacharyya. Tais classes darão origem aos nós descendentes e serão responsáveis por definir a função de decisão SVM. Repete-se este procedimento em todos os nós da árvore, até que reste apenas uma classe por nó, nos chamados nós terminais. Os softwares necessários foram desenvolvidos em ambiente MATLAB e são apresentados na dissertação. Os resultados obtidos nos experimentos permitem concluir que SVM é uma abordagem alternativa válida e eficaz para classificação de imagens hiperespectrais de sensoriamento remoto.<br>This dissertation deals with the application of Support Vector Machines (SVM) to the classification of remote sensing high-dimensional image data. It is well known that in many cases classes that are spectrally very similar and thus not separable when using the more conventional low-dimensional data, can nevertheless be separated with an high degree of accuracy in high dimensional spaces. Classification of high-dimensional image data can, however, become a challenging problem for parametric classifiers such as the well-known Gaussian Maximum Likelihood. A large number of variables produce an also large number of parameters to be estimated from a generally limited number of training samples. This condition causes the Hughes phenomenon which consists in a gradual degradation of the accuracy as the data dimensionality increases beyond a certain value. Non-parametric classifiers present the advantage of being less sensitive to this dimensionality problem. SVM has been receiving a great deal of attention from the international community as an efficient classifier. In this dissertation it is analyzed the performance of SVM when applied to remote sensing hyper-spectral image data. Initially the more theoretical concepts related to SVM are reviewed and discussed. Next, a series of experiments using AVIRIS image data are performed, using different configurations for the classifier. The data covers a test area established by Purdue University and presents a number of classes (agricultural fields) which are spectrally very similar to each other. The classification accuracy produced by different kernels is investigated as a function of the data dimensionality and compared with the one yielded by the well-known Gaussian Maximum Likelihood classifier. As SVM apply to a pair of classes at a time, a multi-stage classifier structured as a binary tree was developed to deal with the multi-class problem. The tree classifier is initially defined by selecting at each node the most separable pair of classes by using the Bhattacharyya distance as a criterion. These two classes will then be used to define the two descending nodes and the corresponding SVM decision function. This operation is performed at every node across the tree, until the terminal nodes are reached. The required software was developed in MATLAB environment and is also presented in this dissertation.
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Books on the topic "Support vector data description"

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Hamel, Lutz. Knowledge discovery with support vector machines. John Wiley & Sons, 2009.

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Boyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Nova Science Publishers, 2011.

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K, Suykens Johan A., Signoretto Marco, and Argyriou Andreas, eds. Regularization, optimization, kernels, and support vector machines. Taylor & Francis, 2014.

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Joachims, Thorsten. Learning to Classify Text Using Support Vector Machines. Springer US, 2002.

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Shi, Feng. Learn About Support Vector Machine in R With Data From the Adult Census Income Dataset (1996). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526495471.

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Shi, Feng. Learn About Support Vector Machine in Python With Data From the Adult Census Income Dataset (1996). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526499585.

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Silver, Mark. Systems that support decision makers: Description and analysis. Wiley, 1991.

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Oberndorfer, Thomas. Computer-aided mining method decision with special emphasis on computer-oriented mining method description. Institut für Bergbaukunde, Bergtechnik und Bergwirtschaft, Montanuniversität Leoben, 1993.

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A, Scovill Cathy, and Defense Systems Management College. Decision Support Systems Directorate., eds. The Program Manager's Support System (PMSS): An executive overview and description of functional modules. 4th ed. Defense Systems Management College, Decision Support Systems Directorate, 1991.

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Defense Systems Management College. Deliberation Support Division, ed. The Program Manager's Support System (PMSS): An executive overview and description of functional modules. 5th ed. Defense Systems Management College, Deliberation Support Division, 1992.

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Book chapters on the topic "Support vector data description"

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Nguyen, Phuoc, Dat Tran, Xu Huang, and Wanli Ma. "Parallel Support Vector Data Description." In Advances in Computational Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38679-4_27.

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GhasemiGol, Mohammad, Reza Monsefi, and Hadi Sadoghi Yazdi. "Ellipse Support Vector Data Description." In Engineering Applications of Neural Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03969-0_24.

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Xuanthanh, Vo, Tran Bach, Hoai An Le Thi, and Tao Pham Dinh. "Ramp Loss Support Vector Data Description." In Intelligent Information and Database Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54472-4_40.

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Fan, Yugang, Ping Li, and Zhihuan Song. "Grid-Based Fuzzy Support Vector Data Description." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_189.

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Saltos, Ramiro, and Richard Weber. "IOWA Rough-Fuzzy Support Vector Data Description." In Information and Communication Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18272-3_18.

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Ghafoori, Zahra, and Christopher Leckie. "Deep Multi-sphere Support Vector Data Description." In Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.13.

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Le, Trung, Dat Tran, and Wanli Ma. "Fuzzy Multi-Sphere Support Vector Data Description." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37456-2_48.

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Le, Trung, Dat Tran, Wanli Ma, and Dharmendra Sharma. "Deterministic Annealing Multi-Sphere Support Vector Data Description." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34487-9_23.

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Wang, Ziqiang, and Xia Sun. "Improving Support Vector Data Description for Document Clustering." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29390-0_44.

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Hao, Pei-Yi. "A New Fuzzy Support Vector Data Description Machine." In Modern Advances in Applied Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07455-9_13.

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Conference papers on the topic "Support vector data description"

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Ghozatlou, Omid, Andrei Anghel, and Mihai Datcu. "Active Learning with Deep Support Vector Data Description for Earth Observation Satellite Image Classification." In 2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging (CoSeRa). IEEE, 2024. http://dx.doi.org/10.1109/cosera60846.2024.10720363.

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Huang, Guang-Xin, Hua-Fu Chen, and Feng Yin. "Improved support vector data description." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580837.

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Zhe Wang and Daqi Gao. "Discriminant Support Vector Data Description." In 2010 Third International Workshop on Advanced Computational Intelligence (IWACI). IEEE, 2010. http://dx.doi.org/10.1109/iwaci.2010.5585155.

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Sohrab, Fahad, Jenni Raitoharju, Moncef Gabbouj, and Alexandros Iosifidis. "Subspace Support Vector Data Description." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545819.

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Gautam, Chandan, and Aruna Tiwari. "Localized Multiple Kernel Support Vector Data Description." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00224.

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Yin, Feng, and Guang-Xin Huang. "Improved density-induced support vector data description." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016770.

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Xie, Weiyi, Stefan Uhlmann, Serkan Kiranyaz, and Moncef Gabbouj. "Incremental Learning with Support Vector Data Description." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.669.

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Le, Trung, Dat Tran, Wanli Ma, and Dharmendra Sharma. "Fuzzy Multi-sphere Support Vector Data Description." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251336.

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Trung Le, Dat Tran, Wanli Ma, and Dharmendra Sharma. "A unified model for support vector machine and support vector data description." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252642.

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Erfani, Masoud, Farzaneh Shoeleh, and Ali A. Ghorbani. "Financial Fraud Detection using Deep Support Vector Data Description." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378256.

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Reports on the topic "Support vector data description"

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Mangasarian, Olvi L. Data Mining via Generalized Support Vector Machines. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada414231.

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Gertz, E. M., and J. D. Griffin. Support vector machine classifiers for large data sets. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/881587.

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O'Neill, Francis, Kristofer Lasko, and Elena Sava. Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45842.

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This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.
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Dritz, K. Software version description (SVD) for the enhanced logistics intratheater support tool (ELIST) global data segment version. 8.1.0.0 for Solaris 7. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/793107.

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Dritz, K. Software version description (SVD) for the enhanced logistics intratheater support tool (ELIST) reference data segment version. 8.1.0.0 for Solaris 7. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/793108.

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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.
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Dritz, K., M. Absil-Mills, and K. Jacobs. Software test plan/description/report (STP/STD/STR) for the enhanced logistics intratheater support tool (ELIST) global data segment. Version 8.1.0.0, Database Instance Segment Version 8.1.0.0, ... [elided] and Reference Data Segment Version 8.1.0.0 for Solaris 7. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/793096.

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Dime, Roselle, Juzhong Zhuang, and Edimon Ginting. Estimating Fiscal Multipliers in Selected Asian Economies. Asian Development Bank, 2021. http://dx.doi.org/10.22617/wps210309-2.

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The surge of the coronavirus disease (COVID-19) pandemic has driven countries worldwide to launch substantial stimulus packages to support economic recovery. This paper estimates effects of fiscal measures on output using data from 2000 to 2019 for a panel of nine developing Asian economies and a vector autoregression model. Results show that (i) the 4-quarter and 8-quarter cumulative fiscal multipliers for general government spending range between 0.73 and 0.88 in baselines, in line with recent estimates for developed countries but larger than those for developing countries; (ii) government spending is more effective than tax cuts in boosting the economy; and (iii) an accommodative monetary policy regime can make fiscal measures more effective.
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Angelelli, Pablo, Rebecca Moudry, and Juan J. Llisterri. Institutional Capacities for Small Business Policy Development in Latin America and the Caribbean. Inter-American Development Bank, 2006. http://dx.doi.org/10.18235/0008873.

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This study provides a quantitative description of MSMEs in Latin America and the Caribbean. It highlights the quality of the information available in the region and, in doing so, also identifies gaps in the data. The study also presents a comparative analysis of the capacity of national MSME institutions to support small business development.
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Baader, Franz, Stefan Borgwardt, and Marcel Lippmann. Temporal Conjunctive Queries in Expressive DLs with Non-simple Roles. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.222.

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In Ontology-Based Data Access (OBDA), user queries are evaluated over a set of facts under the open world assumption, while taking into account background knowledge given in the form of a Description Logic (DL) ontology. Motivated by situation awareness applications, temporal conjunctive queries (TCQs) have recently been proposed as a useful extension of traditional OBDA to support the processing of temporal information. This paper extends the existing complexity analysis of TCQ entailment to very expressive DLs underlying the OWL 2 standard, and in contrast to previous work also allows for queries containing transitive roles.
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