%0 
%0 Book Section
%A Bloehdorn, Stephan & Hotho, Andreas
%D 2008
%T Machine Learning and Ontologies
%E Staab, Steffen & Studer, Rudi
%B Handbook on Ontologies 
%C Berlin--Heidelberg, Germany
%I Springer
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%K ml ontologies 
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%0 
%0 Conference Proceedings
%A Cohen, William W. & Hirsh, Haym
%D 1994
%T Learning the CLASSIC Description Logic: Theoretical and Experimental Results
%E Doyle, Jon; Sandewall, Erik & Torasso, Pietro
%B Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning (KR'94), May 24-27, 1994, Bonn, Germany
%C 
%I Morgan-Kauffman Publishers, San Francisco, CA, USA
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%K DL ml 
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%0 
%0 Journal Article
%A Cover, T. & Hart, P.
%D 1967
%T Nearest neighbor pattern classification
%E 
%B IEEE Transactions on Information Theory
%C 
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%V 13
%6
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%P 21- 27
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%F cover67nearestneighbour
%K algorithms ml seminal 
%X The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of errorRof such a rule must be at least as great as the Bayes probability of errorR^{ast}--the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in theM-category case thatR^{ast} leq R leq R^{ast}(2 --MR^{ast}/(M-1)), where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
%Z 
%U http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1053964
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%0 
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%A Cristianini, Nello & Shawe-Taylor, John
%D 2000
%T An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
%E 
%B 
%C Cambridge, UK
%I Cambridge University Press
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%0 Conference Proceedings
%A Cumby, Chad M. & Roth, Dan
%D 2003
%T Learning with Feature Description Logics
%E Matwin, Stan & Sammut, Claude
%B Proceedings of the 12th International Conference on Inductive Logic Programming (ILP 2002), July 9--11, 2002, Sydney, Australia --- Revised Papers
%C 
%I Springer, Berlin--Heidelberg, Germany
%V 
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%P 32--47
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%S Lecture Notes in Computer Science
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%A Hastie, Trevor; Tibshirani, Robert & Friedman, Jerome
%D 2001
%T The Elements of Statistical Learning
%E 
%B Springer Series in Statistics
%C New York, NY, USA
%I Springer New York Inc.
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%A Lloyd, J.W.
%D 2003
%T Logic for Learning: Learning Comprehensible Theories from Structured Data
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%C Berlin--Heidelberg, Germany
%I Springer
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%A Mitchell, Thomas
%D 1997
%T Machine Learning
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%B 
%C New York, NY, USA
%I McGraw-Hill
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%U http://www.amazon.com/exec/obidos/redirect?tag=citeulike-20\&path=ASIN/0071154671
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%0 
%0 Book
%A Shawe-Taylor, John & Cristianini, Nello
%D 2004
%T Kernel Methods for Pattern Analysis
%E 
%B 
%C Cambridge, UK
%I Cambridge University Press
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%0 
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%A 
%D 2005
%T Proceedings of the Workshop on Learning in Web Search (LWS 2005) 
%E Bloehdorn, Stephan; Buntine, Wray & Hotho, Andreas
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%F 2005-lws-proceedings
%K ir ml sb sb-editor 
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%Z Workshop at the 22nd International Conference on Machine Learning (ICML 2005) 
%U http://cosco.hiit.fi/search/learninginsearch05/ICML_W4.pdf
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