Algorithmic Learning Theory 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings
This volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. In addition to 3 invited papers, 29 papers were selected from 47 submitted extended abstracts. The workshop was the fourth in a series of ALT workshops, whos...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1993, 1993
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| Edition: | 1st ed. 1993 |
| Series: | Lecture Notes in Artificial Intelligence
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- Uniform characterizations of various kinds of language learning
- How to invent characterizable inference methods for regular languages
- Neural Discriminant Analysis
- A new algorithm for automatic configuration of Hidden Markov Models
- On the VC-dimension of depth four threshold circuits and the complexity of Boolean-valued functions
- On the sample complexity of consistent learning with one-sided error
- Complexity of computing Vapnik-Chervonenkis dimension
- ?-approximations of k-label spaces
- Exact learning of linear combinations of monotone terms from function value queries
- Thue systems and DNA — A learning algorithm for a subclass
- The VC-dimensions of finite automata with n states
- Unifying learning methods by colored digraphs
- A perceptual criterion for visually controlling learning
- Learning strategies using decision lists
- A decomposition basedinduction model for discovering concept clusters from databases
- Algebraic structure of some learning systems
- Induction of probabilistic rules based on rough set theory
- Identifying and using patterns in sequential data
- Learning theory toward Genome Informatics
- Optimal layered learning: A PAC approach to incremental sampling
- Reformulation of explanation by linear logic toward logic for explanation
- Towards efficient inductive synthesis of expressions from input/output examples
- A typed ?-calculus for proving-by-example and bottom-up generalization procedure
- Case-based representation and learning of pattern languages
- Inductive resolution
- Generalized unification as background knowledge in learning logic programs
- Inductive inference machines that can refute hypothesis spaces
- On the duality between mechanistic learners and what it is they learn
- On aggregating teams of learning machines
- Learning with growing quality
- Use of reduction arguments in determining Popperian FIN-type learning capabilities
- Properties of language classes with finite elasticity