Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

Bernhard Schölkopf, Alexander J. Smola

買這商品的人也買了...

相關主題

商品描述

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.

商品描述(中文翻譯)

一本全面介紹支持向量機(Support Vector Machines)和相關核方法的書籍。

在1990年代,基於統計學習理論的結果,發展出了一種新型的學習算法:支持向量機(SVM)。這產生了一類理論上優雅的學習機器,它們使用SVM的核心概念-核函數,用於多個學習任務。核機器提供了一個模塊化框架,通過選擇核函數和基本算法,可以適應不同的任務和領域。它們正在取代神經網絡在工程、信息檢索和生物信息學等多個領域的應用。

《使用核函數學習》(Learning with Kernels)提供了對SVM和相關核方法的介紹。雖然這本書從基礎知識開始,但也包含了最新的研究成果。它提供了所有必要的概念,使讀者能夠在具備一些基本數學知識的情況下進入機器學習的世界,使用理論上有基礎但易於使用的核算法,並理解和應用近年來開發的強大算法。