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Titlebook: Inductive Logic Programming; 6th International Wo Stephen Muggleton Conference proceedings 1997 Springer-Verlag Berlin Heidelberg 1997 indu

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书目名称Inductive Logic Programming
副标题6th International Wo
编辑Stephen Muggleton
视频videohttp://file.papertrans.cn/464/463895/463895.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Inductive Logic Programming; 6th International Wo Stephen Muggleton Conference proceedings 1997 Springer-Verlag Berlin Heidelberg 1997 indu
描述This book constitutes the strictly refereed post-workshop proceedings of the 6th International Workshop on Inductive Logic Programming, ILP-96, held in Stockholm, Sweden, in August 1996..The 21 full papers were carefully reviewed and selected for inclusion in the book in revised version. Also included is the invited contribution "Inductive logic programming for natural language processing" by Raymond J. Mooney. Among the topics covered are natural language learning, drug design, NMR and ECG analysis, glaucoma diagnosis, efficiency measures for implementations and database interaction, program synthesis, proof encoding and learning in the absence of negative data, and least generalizations under implication ordering.
出版日期Conference proceedings 1997
关键词inductive learning; inductive logic programming; learning; logic; machine learning; natural language proc
版次1
doihttps://doi.org/10.1007/3-540-63494-0
isbn_softcover978-3-540-63494-2
isbn_ebook978-3-540-69583-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 1997
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978-3-540-63494-2Springer-Verlag Berlin Heidelberg 1997
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Relational knowledge discovery in databases,for adapting and linking ILP-systems to relational database systems. The proposed methods range from “pure ILP” to “based on techniques originating in ILP” We show that it is both easy and advantageous to adapt ILP-systems in this way.
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https://doi.org/10.1007/3-540-63494-0inductive learning; inductive logic programming; learning; logic; machine learning; natural language proc
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Applying ILP to diterpene structure elucidation from 13C NMR spectra,ng was successfully applied to learn classification rules from spectra with assigned atom numbers. As the assignment of atom numbers is a difficult process in itself (and possibly indistinguishable from the classification process), we apply ILP, i.e., relational learning, to the problem of classifyi
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Feature construction with inductive logic programming: A study of quantitative predictions of bioloWhen both are applicable, ILP programs are usually slower than their propositional counterparts. This paper examines the use of ILP programs, not for obtaining theories complete for the sample, but as a method of “discovering” new attributes. These could then be used by methods like linear regressio
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