Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Hardcover)
暫譯: 使用核方法學習：支持向量機、正則化、優化及其他

Bernhard Schlkopf, Alexander J. Smola

Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Hardcover)

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商品描述

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)

在1990年代，基於統計學習理論的研究結果，開發出了一種新的學習演算法：支持向量機（Support Vector Machine, SVM）。這催生了一類理論上優雅的學習機器，這些機器使用SVM的一個核心概念——核（kernels）——來處理多種學習任務。核機器提供了一個模組化的框架，可以通過選擇核函數和基礎演算法來適應不同的任務和領域。它們正在取代神經網絡，應用於包括工程、資訊檢索和生物資訊學等多個領域。

《使用核進行學習》（Learning with Kernels）介紹了SVM及相關的核方法。雖然本書從基礎開始，但也包含了最新的研究成果。它提供了所有必要的概念，使具備一些基本數學知識的讀者能夠進入機器學習的世界，使用理論上有根據且易於使用的核演算法，並理解和應用過去幾年開發的強大演算法。

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