Inductive knowledge acquisition and induction methodologies
作者： Wray Buntine
DOI: 10.1016/0950-7051(89)90008-7
关键词:
摘要: Knowledge acquisition will always remains a key problem in the development of knowledge-based systems. With this motivation, divergent number methodologies and associated issues are appearing literature. This paper looks at how certain induction theories conform to requirements knowledge from light practical experience. Experiments with perceptrons versus 'idiot' Bayes reported, an evaluation Valiant's learning framework is made that yields improved bounds for reliable learning. Finally, some inductive summarized.
elsevier.com
sci-hub.se 参考文章(27)
Paul E. Utgoff, Perceptron trees: a case study in hybrid concept representations national conference on artificial intelligence. pp. 601- 606 ,(1988)
Stuart J. Russell, Tree-structured bias national conference on artificial intelligence. pp. 641- 645 ,(1988)
Wray L. Buntine, Generalised subsumption and its applications to induction and redundancy european conference on artificial intelligence. pp. 15- 23 ,(1986)
Stephen Muggleton, Structuring knowledge by asking questions EWSL'87 Proceedings of the 2nd European Conference on European Working Session on Learning. pp. 218- 229 ,(1987)
Suk Wah Kwok, Chris Carter, Multiple decision trees uncertainty in artificial intelligence. ,vol. 9, pp. 327- 338 ,(1990) , 10.1016/B978-0-444-88650-7.50030-5
JONATHAN AMSTERDAM, Extending the Valiant Learning Model international conference on machine learning. pp. 381- 394 ,(1988) , 10.1016/B978-0-934613-64-4.50044-X
J.R. QUINLAN, An Empirical Comparison of Genetic and Decision-Tree Classifiers international conference on machine learning. pp. 135- 141 ,(1988) , 10.1016/B978-0-934613-64-4.50019-0
Alen D. Shapiro, Structured induction in expert systems ,(1987)
Julius T. Tou, Rafael C. Gonzalez, Pattern recognition principles ,(1974)
Chris Carter, Jason Catlett, Assessing Credit Card Applications Using Machine Learning IEEE Intelligent Systems. ,vol. 2, pp. 71- 79 ,(1987) , 10.1109/MEX.1987.4307093