Algorithmic Learning Theory
4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings
Published on 20. October 1993
Book
Paperback/Softback
XI, 428 pages
978-3-540-57370-8 (ISBN)
Description
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,
whose focus is on theories of machine learning and the
application of such theories to real-world learning
problems. The ALT workshops have been held annually since
1990, sponsored by the Japanese Society for Artificial
Intelligence. The volume is organized into parts on
inductive logic and inference, inductive inference,
approximate learning, query learning, explanation-based
learning, and new learning paradigms.
More details
Series
Edition
1993 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XI, 428 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 24 mm
Weight
668 gr
ISBN-13
978-3-540-57370-8 (9783540573708)
DOI
10.1007/3-540-57370-4
Schweitzer Classification
Content
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.- 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.