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Download book Knowledge Acquisition: Selected Research and Commentary : A Special Issue of Machine Learning on Knowledge Acquisition

Download book Knowledge Acquisition: Selected Research and Commentary : A Special Issue of Machine Learning on Knowledge Acquisition

Knowledge Acquisition: Selected Research and Commentary : A Special Issue of Machine Learning on Knowledge Acquisition. S. Marcus

Knowledge Acquisition: Selected Research and Commentary : A Special Issue of Machine Learning on Knowledge Acquisition
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Author: S. Marcus
Number of Pages: 152 pages
Published Date: 21 Sep 2011
Publisher: Springer-Verlag New York Inc.
Publication Country: New York, NY, United States
Language: English
Type: eBook
ISBN: 9781461288213
File size: 26 Mb
File Name: Knowledge.Acquisition.Selected.Research.and.Commentary.A.Special.Issue.of.Machine.Learning.on.Knowledge.Acquisition.pdf
Download Link: Knowledge Acquisition Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition
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What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro- duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base.

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