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MIT Press Learning with Kernels - Support Vector Machines Book

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Description

Explore the fundamental principles of Support Vector Machines (SVM) with this comprehensive guide from MIT Press. Part of the Adaptive Computation and Machine Learning series, this book provides a deep introduction to kernel methods and statistical learning theory. Since their development in the 1990s, SVMs have introduced a class of elegant learning machines that utilize kernels to perform various learning tasks. This text explains how kernel machines offer a modular framework, allowing for adaptation to different domains and tasks through the selection of specific kernel functions and base algorithms. Learn why these methods are increasingly used in place of neural networks across many different fields.

Key Features

This MIT Press publication provides a comprehensive introduction to Support Vector Machines and related kernel methods.

The text covers essential topics including regularization, optimization, and statistical learning theory.

Learn how kernel machines function as a modular framework that can be adapted to various tasks and domains.

Explore the role of kernel functions and base algorithms in modern machine learning applications.

Part of the Adaptive Computation and Machine Learning series for specialized technical study.

Product Specifications

Colour
Multicolor
Format
paperback
Domain
Amazon UK
Release Date
05 June 2018
Listed Since
06 June 2018

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