Concept formation in structured domains
作者： KEVIN THOMPSON , PAT LANGLEY
DOI: 10.1016/B978-1-4832-0773-5.50011-0
关键词: Representation language 、 Concept learning 、 Matching (graph theory) 、 Mathematics 、 Artificial intelligence 、 Simple language 、 Probabilistic logic 、 Natural (music) 、 Structure (mathematical logic) 、 Theoretical computer science
摘要: Publisher Summary This chapter describes a system that learns concepts in structured domains. Most recent work on unsupervised concept learning has been limited to unstructured domains, which instances are described by fixed sets of attribute-value pairs. Many domains can be this simple language. Frequently, however, have some natural structure; objects components and relations among those components. In such an language is inadequate. The Labyrinth, implemented induces from objects. Labyrinth viewed as approach incremental formation. goal formation find allow useful predictions partial information. make effective generalizations using more powerful representation It carries out incremental, probabilistic uses them missing attribute values, components, relations. also decomposes into constrain matching.
sci-hub.st 参考文章(29)
Douglas Fisher, A proposed method of conceptual clustering for structured and decomposable objects Machine learning: a guide to current research. pp. 67- 70 ,(1986) , 10.1007/978-1-4613-2279-5_16
Paul J. Kline, The superiority of relative criteria in partial matching and generalization international joint conference on artificial intelligence. pp. 296- 303 ,(1981)
Douglas Fisher, Pat Langley, The Structure and Formation of Natural Categories Psychology of Learning and Motivation. ,vol. 26, pp. 241- 284 ,(1990) , 10.1016/S0079-7421(08)60056-0
Patrick H. Winston, Learning Structural Descriptions From Examples The Psychology of Computer Vision. ,(1970)
Ryszard S. Michalski, Robert E. Stepp, Learning from Observation: Conceptual Clustering Machine Learning. pp. 331- 363 ,(1983) , 10.1007/978-3-662-12405-5_11
Thomas G. Dietterich, Ryszard S. Michalski, Inductive learning of structural descriptions: Evaluation criteria and comparative review of selected methods Artificial Intelligence. ,vol. 16, pp. 257- 294 ,(1981) , 10.1016/0004-3702(81)90002-3
Bernd Nordhausen, Pat Langley, An Integrated Approach to Empirical Discovery Computational Models of Scientific Discovery and Theory Formation. pp. 97- 128 ,(1989) , 10.21236/ADA212175
John H. Gennari, Focused concept formation international conference on machine learning. pp. 379- 382 ,(1989) , 10.1016/B978-1-55860-036-2.50096-5
Pat Langley, James Wogulis, Improving efficiency by learning intermediate concepts international joint conference on artificial intelligence. pp. 657- 662 ,(1989)
Ryszard S. Michalski, A theory and methodology of inductive learning Computer Compacts. ,vol. 1, pp. 49- ,(1983) , 10.1016/0167-7136(83)90132-4