作者: Bernhard Schlkopf / Alexander J·Smola出版社: The MIT Press
基本介紹
- 書名：Learning with Kernels（核學習）
- 作者： Bernhard Schlkopf / Alexander J·Smola
- ISBN：9780262194754
- 頁數：648
- 出版社：The MIT Press
- 出版時間：2001-12-15
- 裝幀：精裝
內容簡介
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.
在20世紀90年代，一種新型的學習算法的開發，統計學習理論的基礎上：支持向量機（SVM）。這引起了一類新的理論上優雅的學習機，使用一個中心的概念，支持向量機-核心-一些學習任務。核心機器提供了一個模組化的框架，可以適應不同的任務和域的核心函式和基本算法的選擇。他們正在更換神經網路的各種領域，包括工程，信息檢索和生物信息學與核心學習提供了一個介紹支持向量機和相關的核心方法。雖然這本書從基礎開始，它也包括最新的研究。它提供了所有必要的概念使讀者具備一些基本的數學知識進入機使用理論上成立的且易於使用的核心算法和理解和套用，已在過去的幾年開發了強大的算法學習的世界。
