An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

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Author: Nello Cristianini,John Shawe-Taylor

Publisher: Cambridge University Press

ISBN: 9780521780193

Category: Computers

Page: 189

View: 1838

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications.

Learning with Kernels

Support Vector Machines, Regularization, Optimization, and Beyond

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Author: Bernhard Schölkopf,Alexander J. Smola,Managing Director of the Max Planck Institute for Biological Cybernetics in Tubingen Germany Profe Bernhard Scholkopf,Francis Bach

Publisher: MIT Press

ISBN: 9780262194754

Category: Computers

Page: 626

View: 7579

This volume provides an introduction to SVMs and related kernel methods. It provides concepts necessary to enable a reader to enter the world of machine learning using theoretical kernel algorithms and to understand and apply the algorithms that have been developed over the last few years.

Machine Learning with SVM and Other Kernel Methods

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Author: K.P. Soman,R. LOGANATHAN,V. AJAY

Publisher: PHI Learning Pvt. Ltd.

ISBN: 8120334353

Category: Computers

Page: 486

View: 5681

Support vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory. Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to solve any pattern classification problems with ease and that too in Excel. KEY FEATURES  Extensive coverage of Lagrangian duality and iterative methods for optimization  Separate chapters on kernel based spectral clustering, text mining, and other applications in computational linguistics and speech processing  A chapter on latest sequential minimization algorithms and its modifications to do online learning  Step-by-step method of solving the SVM based classification problem in Excel.  Kernel versions of PCA, CCA and ICA The CD accompanying the book includes animations on solving SVM training problem in Microsoft EXCEL and by using SVMLight software . In addition, Matlab codes are given for all the formulations of SVM along with the data sets mentioned in the exercise section of each chapter.

Support Vector Machines: Theory and Applications

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Author: Lipo Wang

Publisher: Springer Science & Business Media

ISBN: 9783540243885

Category: Computers

Page: 431

View: 4664

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.

A Gentle Introduction to Support Vector Machines in Biomedicine

Volume 1: Theory and Methods

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Author: Alexander Statnikov,Constantin F Aliferis,Douglas P Hardin,Isabelle Guyon

Publisher: World Scientific Publishing Company

ISBN: 9813107995

Category: Computers

Page: 200

View: 6379

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).

An Introduction to Kernel Based Learning Algorithms, Neural Networks, 2001

An Introduction to Kernel Based Learning Algorithms

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Author: Miller-Mika-Ratsch-Tsuda-Scholkopf.

Publisher: Bukupedia

ISBN: N.A

Category: Computers

Page: 22

View: 4836

An Introduction to Kernel Based Learning Algorithms, Neural Networks, 2001 Miller-Mika-Ratsch-Tsuda-Scholkopf. - Computers / Programming / Algorithms.

Data Mining Algorithms

Explained Using R

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Author: Pawel Cichosz

Publisher: John Wiley & Sons

ISBN: 1118950801

Category: Mathematics

Page: 720

View: 3163

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

Learning with Support Vector Machines

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Author: Colin Campbell,Yiming Ying

Publisher: Morgan & Claypool Publishers

ISBN: 1608456161

Category: Computers

Page: 100

View: 7762

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels

Support Vector Machines

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Author: Ingo Steinwart,Andreas Christmann

Publisher: Springer Science & Business Media

ISBN: 0387772421

Category: Computers

Page: 601

View: 6829

Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni?ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the?eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others.

Advances in Large Margin Classifiers

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Author: Alexander J. Smola,Peter J. Bartlett,Dale Schuurmans,Bernhard Schölkopf

Publisher: MIT Press

ISBN: 9780262194488

Category: Computers

Page: 412

View: 6860

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.