Machine Learning With Svm And Other Kernel Methods

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Machine Learning With Svm And Other Kernel Methods

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Author : K.P. Soman
language : en
Publisher:
Release Date : 2011

Machine Learning With Svm And Other Kernel Methods written by K.P. Soman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Neural networks (Computer science) categories.

Machine Learning With Svm And Other Kernel Methods

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Author : K.P. Soman
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2009-02-02

Machine Learning With Svm And Other Kernel Methods written by K.P. Soman and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-02 with Computers categories.

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.

An Introduction To Support Vector Machines And Other Kernel Based Learning Methods

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Author : Nello Cristianini
language : en
Publisher: Cambridge University Press
Release Date : 2000-03-23

An Introduction To Support Vector Machines And Other Kernel Based Learning Methods written by Nello Cristianini and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-03-23 with Computers categories.

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

Learning With Kernels

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Author : Bernhard Schölkopf
language : en
Publisher: MIT Press
Release Date : 2002

Learning With Kernels written by Bernhard Schölkopf and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.

A comprehensive introduction to Support Vector Machines and related kernel methods.

Kernel Methods And Machine Learning

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Author : S. Y. Kung
language : en
Publisher: Cambridge University Press
Release Date : 2014-04-17

Kernel Methods And Machine Learning written by S. Y. Kung and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-17 with Computers categories.

Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

An Introduction To Support Vector Machines And Other Kernel Based Learning Methods

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Author : Nello Cristianini
language : en
Publisher: Cambridge University Press
Release Date : 2000-03-23

An Introduction To Support Vector Machines And Other Kernel Based Learning Methods written by Nello Cristianini and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-03-23 with Computers categories.

This is the first comprehensive introduction to Support Vector Machines (SVMs), a 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. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

Kernel Methods In Computational Biology

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Author : Bernhard Schölkopf
language : en
Publisher: MIT Press
Release Date : 2004

Kernel Methods In Computational Biology written by Bernhard Schölkopf and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.

A detailed overview of current research in kernel methods and their application to computational biology.

Imbalanced Learning

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Author : Haibo He
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-07

Imbalanced Learning written by Haibo He and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-07 with Technology & Engineering categories.

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Deterministic And Statistical Methods In Machine Learning

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Author : Joab Winkler
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-10-11

Deterministic And Statistical Methods In Machine Learning written by Joab Winkler and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-11 with Computers categories.

This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004. The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.

Machine Learning Ecml 2006

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Author : Johannes Fürnkranz
language : en
Publisher: Springer
Release Date : 2006-09-21

Machine Learning Ecml 2006 written by Johannes Fürnkranz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-21 with Computers categories.

This book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held, jointly with PKDD 2006. The book presents 46 revised full papers and 36 revised short papers together with abstracts of 5 invited talks, carefully reviewed and selected from 564 papers submitted. The papers present a wealth of new results in the area and address all current issues in machine learning.