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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
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Inductive inference of approximations for recursive concepts
| Title: | Inductive inference of approximations for recursive concepts |
| Authors: | Langea, Steffen Browse this author | | Grieserb, Gunter Browse this author | | Zeugmann, Thomas Browse this author |
| Keywords: | Learning theory | | Inductive inference | | Learning with anomalies | | Conservative learning | | Set-driven learning | | Indexed families | | Learning from examples | | Characterization theorems |
| Issue Date: | 2-Dec-2005 |
| Publisher: | Elsevier B.V. |
| Journal Title: | Theoretical Computer Science |
| Volume: | 348 |
| Issue: | 1 |
| Start Page: | 15 |
| End Page: | 40 |
| Publisher DOI: | 10.1016/j.tcs.2005.09.004 |
| Abstract: | This paper provides a systematic study of inductive inference of indexable concept classes in learning scenarios where the learner is successful if its final hypothesis describes a finite variant of the target concept, i.e., learning with anomalies. Learning from positive data only and from both positive and negative data is distinguished.
The following learning models are studied: learning in the limit, finite identification, set-driven learning, conservative inference, and behaviorally correct learning.
The attention is focused on the case that the number of allowed anomalies is finite but not a priori bounded. However, results for the special case of learning with an a priori bounded number of anomalies are presented, too. Characterizations of the learning models with anomalies in terms of finite tell-tale sets are provided. The observed varieties in the degree of recursiveness of the relevant tell-tale sets are already sufficient to quantify the differences in the corresponding learning models with anomalies. Finally, a complete picture concerning the relations of all models of learning with and without anomalies mentioned above is derived. |
| Relation: | http://www.sciencedirect.com/science/journal/03043975 |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/17147 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: Zeugmann Thomas
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