Learning Theory And Kernel Machines
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Learning Theory and Kernel Machines
Author | : Bernhard Schölkopf,Manfred K. Warmuth |
Publsiher | : Springer |
Total Pages | : 754 |
Release | : 2003-11-11 |
Genre | : Computers |
ISBN | : 9783540451679 |
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This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Learning with Kernels
Author | : Bernhard Scholkopf,Alexander J. Smola |
Publsiher | : MIT Press |
Total Pages | : 645 |
Release | : 2018-06-05 |
Genre | : Computers |
ISBN | : 9780262536578 |
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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.
Learning Theory and Kernel Machines
Author | : Bernhard Schölkopf,Manfred K. Warmuth |
Publsiher | : Springer |
Total Pages | : 0 |
Release | : 2003-11-11 |
Genre | : Computers |
ISBN | : 3540451676 |
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Kernel Methods and Machine Learning
Author | : S. Y. Kung |
Publsiher | : Cambridge University Press |
Total Pages | : 617 |
Release | : 2014-04-17 |
Genre | : Computers |
ISBN | : 9781107024960 |
Download Kernel Methods and Machine Learning Book in PDF, Epub and Kindle
Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.
Learning Kernel Classifiers
Author | : Ralf Herbrich |
Publsiher | : MIT Press |
Total Pages | : 393 |
Release | : 2022-11-01 |
Genre | : Computers |
ISBN | : 9780262546591 |
Download Learning Kernel Classifiers Book in PDF, Epub and Kindle
An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Learning Theory and Kernel Machines
![Learning Theory and Kernel Machines](https://youbookinc.com/wp-content/uploads/2024/06/cover.jpg)
Author | : Anonim |
Publsiher | : Unknown |
Total Pages | : 135 |
Release | : 2003 |
Genre | : Electronic Book |
ISBN | : OCLC:771328644 |
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Learning Theory and Kernel Machines
Author | : Bernhard Schoelkopf,Manfred K. Warmuth |
Publsiher | : Springer Science & Business Media |
Total Pages | : 761 |
Release | : 2003-08-11 |
Genre | : Computers |
ISBN | : 9783540407201 |
Download Learning Theory and Kernel Machines Book in PDF, Epub and Kindle
This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
An Introduction to Support Vector Machines and Other Kernel based Learning Methods
Author | : Nello Cristianini,John Shawe-Taylor |
Publsiher | : Cambridge University Press |
Total Pages | : 216 |
Release | : 2000-03-23 |
Genre | : Computers |
ISBN | : 0521780195 |
Download An Introduction to Support Vector Machines and Other Kernel based Learning Methods Book in PDF, Epub and Kindle
This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.