Pattern Recognition With Support Vector Machines

Author : Seong-Whan Lee,Alessandro Verri
Publisher : Springer Science & Business Media
Page : 433 pages
File Size : 41,7 Mb
Release : 2002-07-29
Category : Computers
ISBN : 9783540440161

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Pattern Recognition with Support Vector Machines by Seong-Whan Lee,Alessandro Verri Pdf

This book constitutes the refereed proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002, held in Niagara Falls, Canada in August 2002. The 16 revised full papers and 14 poster papers presented together with two invited contributions were carefully reviewed and selected from 57 full paper submissions. The papers presented span the whole range of topics in pattern recognition with support vector machines from computational theories to implementations and applications.

Author : Shigeo Abe
Publisher : Springer Science & Business Media
Page : 332 pages
File Size : 53,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447102854

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Pattern Classification by Shigeo Abe Pdf

This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.

Author : Lipo Wang
Publisher : Springer Science & Business Media
Page : 456 pages
File Size : 50,7 Mb
Release : 2005-06-21
Category : Computers
ISBN : 3540243887

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Support Vector Machines: Theory and Applications by Lipo Wang Pdf

The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.

Author : Shigeo Abe
Publisher : Springer Science & Business Media
Page : 350 pages
File Size : 46,5 Mb
Release : 2005-12-28
Category : Computers
ISBN : 9781846282195

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Support Vector Machines for Pattern Classification by Shigeo Abe Pdf

I was shocked to see a student’s report on performance comparisons between support vector machines (SVMs) and fuzzy classi?ers that we had developed withourbestendeavors.Classi?cationperformanceofourfuzzyclassi?erswas comparable, but in most cases inferior, to that of support vector machines. This tendency was especially evident when the numbers of class data were small. I shifted my research e?orts from developing fuzzy classi?ers with high generalization ability to developing support vector machine–based classi?ers. This book focuses on the application of support vector machines to p- tern classi?cation. Speci?cally, we discuss the properties of support vector machines that are useful for pattern classi?cation applications, several m- ticlass models, and variants of support vector machines. To clarify their - plicability to real-world problems, we compare performance of most models discussed in the book using real-world benchmark data. Readers interested in the theoretical aspect of support vector machines should refer to books such as [109, 215, 256, 257].

Author : Yunqian Ma,Guodong Guo
Publisher : Springer Science & Business Media
Page : 306 pages
File Size : 51,6 Mb
Release : 2014-02-12
Category : Technology & Engineering
ISBN : 9783319023007

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Support Vector Machines Applications by Yunqian Ma,Guodong Guo Pdf

Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.

Author : Alessandro Verri
Publisher : Unknown
Page : 0 pages
File Size : 41,8 Mb
Release : 2002
Category : Electronic
ISBN : OCLC:474932016

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Pattern Recognition with Support Vector Machines by Alessandro Verri Pdf

Author : Thorsten Joachims
Publisher : Springer Science & Business Media
Page : 218 pages
File Size : 53,8 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461509073

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Learning to Classify Text Using Support Vector Machines by Thorsten Joachims Pdf

Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.

Author : Seong-Whan Lee,Alessandro Verri
Publisher : Springer
Page : 428 pages
File Size : 51,9 Mb
Release : 2014-03-12
Category : Computers
ISBN : 3662187922

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Pattern Recognition with Support Vector Machines by Seong-Whan Lee,Alessandro Verri Pdf

Author : Alexander Statnikov
Publisher : World Scientific
Page : 200 pages
File Size : 46,6 Mb
Release : 2011
Category : Computers
ISBN : 9789814324380

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A Gentle Introduction to Support Vector Machines in Biomedicine: Theory and methods by Alexander Statnikov Pdf

Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and cases studies (Volume 2).The proposed book follows the approach of ?programmed learning? whereby material is presented in short sections called ?frames?. Each frame consists of a very small amount of information to be learned, a multiple choice quiz, and answers to the quiz. The reader can proceed to the next frame only after verifying the correct answers to the current frame.

Author : Shigeo Abe
Publisher : Springer Science & Business Media
Page : 486 pages
File Size : 41,9 Mb
Release : 2010-07-23
Category : Technology & Engineering
ISBN : 9781849960984

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Support Vector Machines for Pattern Classification by Shigeo Abe Pdf

A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.

Author : Nello Cristianini,John Shawe-Taylor
Publisher : Cambridge University Press
Page : 216 pages
File Size : 45,8 Mb
Release : 2000-03-23
Category : Computers
ISBN : 0521780195

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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini,John Shawe-Taylor Pdf

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

Author : Alexander Statnikov,Constantin F Aliferis,Douglas P Hardin,Isabelle Guyon
Publisher : World Scientific Publishing Company
Page : 212 pages
File Size : 55,5 Mb
Release : 2013-03-21
Category : Computers
ISBN : 9789814518505

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A Gentle Introduction to Support Vector Machines in Biomedicine by Alexander Statnikov,Constantin F Aliferis,Douglas P Hardin,Isabelle Guyon Pdf

Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).

Author : Johan A. K. Suykens,Tony Van Gestel,Jos De Brabanter
Publisher : World Scientific
Page : 318 pages
File Size : 40,6 Mb
Release : 2002
Category : Mathematics
ISBN : 9812381511

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Least Squares Support Vector Machines by Johan A. K. Suykens,Tony Van Gestel,Jos De Brabanter Pdf

This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.

Author : Bernhard Scholkopf,Alexander J. Smola
Publisher : MIT Press
Page : 645 pages
File Size : 44,5 Mb
Release : 2018-06-05
Category : Computers
ISBN : 9780262536578

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Learning with Kernels by Bernhard Scholkopf,Alexander J. Smola Pdf

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Author : Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou
Publisher : CRC Press
Page : 528 pages
File Size : 52,6 Mb
Release : 2014-10-23
Category : Computers
ISBN : 9781482241396

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Regularization, Optimization, Kernels, and Support Vector Machines by Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou Pdf

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.