Learning with Kernels pdf epub mobi txt 电子书 下载 2025
Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.
- 机器学习
- 人工智能
- 计算机
- TML
- ML
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.
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Even it's been published for many years, the majority materials really provide a detail introduction of kernel methods........
评分Even it's been published for many years, the majority materials really provide a detail introduction of kernel methods........
评分Even it's been published for many years, the majority materials really provide a detail introduction of kernel methods........
评分It is an excellent book about learning with kernels. Another issue related to kernels is learning kernels, not learning with kernels. Kernel learning has a long history in research and is important in SVM because it has pretty theoretical properties.
评分This book is a good introductory material for kernel-based machine learning tools. The first part provides an reviews on the required mathematic tools in decision theory (risk and lost functions), statical learning theory and optimization theory. I strongly...
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