Towards realistic theories of learning
- Naoki Abe
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In computational learning theory continuous efforts are made to formulate models of machine learning that are more realistic than previously available models. Two of the most popular models that have been recently proposed, Valiant’s PAC learning model and Angluin’s query learning model, can be thought of as refinements of preceding models such as Gold’s classic paradigm of identification in the limit, in which the question ofhow fast the learning can take place is emphasized. A considerable amount of results have been obtained within these two frameworks, resolving the learnability questions of many important classes of functions and languages. These two particular learning models are by no means comprehensive, and many important aspects of learning are not directly addressed in these models. Aiming towards more realistic theories of learning, many new models and extensions of existing learning models that attempt to formalize such aspects have been developed recently. In this paper, we will review some of these new extensions and models in computational learning theory, concentrating in particular on those proposed and studied by researchers at Theory NEC Laboratory RWCP, and their colleagues at other institutions.
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- Towards realistic theories of learning
New Generation Computing
Volume 15, Issue 1 , pp 3-25
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- Computational Learning Theory
- Machine Learning
- Genetic Information Processing
- Probabilistic PAC Learning
- On-line Learning of Rational Choice
- Population Learning
- Query Learning
- Industry Sectors
- Naoki Abe (1)
- Author Affiliations
- 1. Theory NEC Laboratory, RWCP c/o C&C Research Laboratories, NEC Corporation, 4-1-1 Miyazaki, Miyamae-ku, 216, Kawasaki, Japan