Cantitate/Preț
Produs

Algorithmic Learning Theory: 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993. Proceedings: Lecture Notes in Computer Science, cartea 744

Editat de Klaus P. Jantke, Shigenobu Kobayashi, Etsuji Tomita, Takashi Yokomori
en Limba Engleză Paperback – 20 oct 1993
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.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 30792 lei

Preț vechi: 38491 lei
-20%

Puncte Express: 462

Preț estimativ în valută:
5899 6390$ 5059£

Carte tipărită la comandă

Livrare economică 06-13 mai

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783540573708
ISBN-10: 3540573704
Pagini: 444
Ilustrații: XI, 428 p.
Dimensiuni: 155 x 233 x 23 mm
Greutate: 0.62 kg
Ediția:1993
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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 based induction model for discovering concept clusters from databases.- Algebraic structure of some learning systems.- Induction of probabilistic rules based on rough set theory.