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Published **December 15, 2001**
by The MIT Press .

Written in English

- Mathematical theory of computation,
- Algorithms (Computer Programming),
- Computers,
- Computers - Other Applications,
- Computer Books: General,
- Artificial Intelligence - General,
- Computer Science,
- Computers / Computer Science,
- Programming - General,
- Algorithms,
- Machine Learning

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 384 |

ID Numbers | |

Open Library | OL9897848M |

ISBN 10 | 026208306X |

ISBN 10 | 9780262083065 |

This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. Learning Kernel Classiﬁersis an authoritative treatment of support vector ma- chinesandrelatedkernelclassiﬁcation andregression kexamines these methods both from an algorithmic perspective and from the point of view of learning theory. An overview of the theory and application of kernel classification classifiers in kernel spaces have emerged as a major topic within the field of machine learning.3/5. 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 3/5(3).

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 Cited by: The book is a good reference for scientists and engineers interested in learning about kernel classifiers. It is not very suitable as a primary student text, but is recommended as secondary reading. ♦Learn K(K-1)/2 SV classifiers. ♦Classifier f i,j is learned using only the training samples from the class i and j, labeling +1 and -1, respectively. ♦For a new test object x∈X, the frequency n i of “wins” for class i is computed by applying f i,j for all j. ♦The final decision rule. y y Y fmultiple x n ∈ () =argmax. In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in many algorithms that solve these tasks, the data in raw.

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 sequen. Learning with Kernels. MIT Press, Cambridge, MA, An introduction and overview over SVMs. A free sample of one third of the chapters (Introduction, Kernels, Loss Functions, Optimization, Learning Theory Part I, and Classification) is available on the book website. ( pages, $60). Ralf Herbrich. Learning Kernel Classifiers. MIT Press. FAST KERNEL CLASSIFIERS WITH ONLINE AND ACTIVE LEARNING corresponding feature function Φ(x). For instance, the well known RBF kernel K(x,y) = e−γkx−yk2 deﬁnes an implicit feature space of inﬁnite dimension. A comprehensive introduction to Support Vector Machines and related kernel methods. In the s, 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 SVMskernels--for a number of learning tasks/5(9).

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