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Algorithmic Learning Theory: 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) - Kobayashi, Shigenobu, Etsuji Tomita and Takashi Yokomori
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4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings, Buch, Softcover, [PU: Springer Berlin], Springer Berlin, 1993

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Algorithmic Learning Theory: 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings Klaus P. Jantke Editor
Auteur:
Titre:
ISBN:

3540573704


This volume contains all the papers that were presented atthe Fourth Workshop on Algorithmic Learning Theory, held inTokyo in November 1993. In addition to 3 invited papers, 29papers 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 theapplication of such theories to real-world learningproblems. The ALT workshops have been held annually since1990, sponsored by the Japanese Society for ArtificialIntelligence. The volume is organized into parts oninductive logic and inference, inductive inference,approximate learning, query learning, explanation-basedlearning, and new learning paradigms.

Informations détaillées sur le livre - Algorithmic Learning Theory: 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings Klaus P. Jantke Editor


EAN (ISBN-13): 9783540573708
ISBN (ISBN-10): 3540573704
Version reliée
Livre de poche
Date de parution: 1993
Editeur: Springer Berlin Heidelberg Core >1 >T
444 Pages
Poids: 0,666 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2007-10-27T23:31:43+02:00 (Zurich)
Page de détail modifiée en dernier sur 2023-12-24T06:07:37+01:00 (Zurich)
ISBN/EAN: 3540573704

ISBN - Autres types d'écriture:
3-540-57370-4, 978-3-540-57370-8
Autres types d'écriture et termes associés:
Auteur du livre: tomita, takashi, learning international, shigenobu kobayashi, shinohara, ets, tom, klaus held
Titre du livre: workshop 1993, ing, algorithmic learning theory, alt japan, tokyo certain, lecture notes artificial intelligence, computer

Données de l'éditeur

Auteur: Klaus P. Jantke; Shigenobu Kobayashi; Etsuji Tomita; Takashi Yokomori
Titre: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Algorithmic Learning Theory - 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings
Editeur: Springer; Springer Berlin
428 Pages
Date de parution: 1993-10-20
Berlin; Heidelberg; DE
Langue: Anglais
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
XI, 428 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; algorithm; algorithmic learning theory; algorithms; artificial intelligence; intelligence; learning; learning theory; logic; machine learning; Artificial Intelligence; Mathematics; Theory of Computation; Mathematik; Theoretische Informatik; EA

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.
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