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Algorithmic Learning Theory 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings

Aus der Reihe Lecture Notes in Computer Science

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.10.1993

Abbildungen

XI, 428 p.

Herausgeber

Klaus P. Jantke + weitere

Verlag

Springer Berlin

Seitenzahl

428

Maße (L/B/H)

23,5/15,5/2,4 cm

Gewicht

668 g

Auflage

1993

Sprache

Englisch

ISBN

978-3-540-57370-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.10.1993

Abbildungen

XI, 428 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

428

Maße (L/B/H)

23,5/15,5/2,4 cm

Gewicht

668 g

Auflage

1993

Sprache

Englisch

ISBN

978-3-540-57370-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Algorithmic Learning Theory
  • Produktbild: Algorithmic Learning 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.- 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.