Learning DNF by decision trees
作者： Giulia Pagallo
DOI:
关键词:
摘要: We investigate the problem of learning DNF concepts from examples using decision trees as a concept description language. Due to replication problem, do not always have concise tree when tests at nodes are limited initial attributes. However, representational complexity may be overcome by high level attributes tests. present novel algorithm that modifies bias determined primitive adaptively enlarging attribute set with show empirically this outperforms standard for small random and without noise, drawn uniform distribution.
ijcai.org参考文章(12)
J. R. Quinlan, Generating production rules from decision trees international joint conference on artificial intelligence. pp. 304- 307 ,(1987)
Paul E Utgoff, Tom M Mitchell, None, Acquisition of appropriate bias for inductive concept learning national conference on artificial intelligence. pp. 414- 417 ,(1982)
Vladimir Naumovich Vapnik, Estimation of Dependences Based on Empirical Data ,(2010)
Stephen Muggleton, Duce, an oracle-based approach to constructive induction international joint conference on artificial intelligence. pp. 287- 292 ,(1987)
David Haussler, Quantifying inductive bias: AI learning algorithms and Valiant's learning framework Artificial Intelligence. ,vol. 36, pp. 177- 221 ,(1988) , 10.1016/0004-3702(88)90002-1
Stewart W. Wilson, Classifier Systems and the Animat Problem Machine Learning. ,vol. 2, pp. 199- 228 ,(1987) , 10.1023/A:1022655214215
Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth, Occam's razor Information Processing Letters. ,vol. 24, pp. 377- 380 ,(1987) , 10.1016/0020-0190(87)90114-1
Jeffrey Curtis Schlimmer, Concept acquisition through representational adjustment University of California, Irvine. ,(1987)
J.R. Quinlan, Simplifying decision trees International Journal of Human-computer Studies \/ International Journal of Man-machine Studies. ,vol. 51, pp. 221- 234 ,(1987) , 10.1016/S0020-7373(87)80053-6
Gerald Tesauro, Subutai Ahmad, Scaling and Generalization in Neural Networks: A Case Study neural information processing systems. ,vol. 1, pp. 160- 168 ,(1988)