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Machine Learning: From Theory to Applications
Stephen J. HansonWerner RemmeleRonald L. Rivest
其他書名
Cooperative Research at Siemens and MIT
出版Springer Science & Business Media, 1993-03-30
主題Computers / Artificial Intelligence / GeneralComputers / Computer ArchitectureComputers / Computer ScienceComputers / Information TechnologyComputers / Machine TheoryComputers / Data Science / Neural NetworksComputers / Software Development & Engineering / GeneralComputers / User InterfacesComputers / Data Science / Machine LearningMathematics / Discrete MathematicsScience / General
ISBN35405648379783540564836
URLhttp://books.google.com.hk/books?id=nUO0KGWexBgC&hl=&source=gbs_api
EBookSAMPLE
註釋This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.