Machine Learning - EWSL-91: European Working Session on Learning, Porto, Portugal, March 6-8, 1991. Proceedings: Lecture Notes in Computer Science, cartea 482
Editat de Yves Kodratoffen Limba Engleză Paperback – 20 feb 1991
Din seria Lecture Notes in Computer Science
- 20% Preț: 1061.55 lei
- 20% Preț: 307.71 lei
- 20% Preț: 438.69 lei
- 20% Preț: 645.28 lei
- Preț: 410.88 lei
- 15% Preț: 580.46 lei
- 17% Preț: 427.22 lei
- 20% Preț: 596.46 lei
- Preț: 381.21 lei
- 20% Preț: 353.50 lei
- 20% Preț: 1414.79 lei
- 20% Preț: 309.90 lei
- 20% Preț: 583.40 lei
- 20% Preț: 1075.26 lei
- 20% Preț: 310.26 lei
- 20% Preț: 655.02 lei
- 20% Preț: 580.93 lei
- 20% Preț: 340.32 lei
- 15% Preț: 438.59 lei
- 20% Preț: 591.51 lei
- 20% Preț: 649.49 lei
- 20% Preț: 337.00 lei
- Preț: 449.57 lei
- 20% Preț: 607.39 lei
- 20% Preț: 1024.44 lei
- 20% Preț: 579.30 lei
- 20% Preț: 763.23 lei
- 20% Preț: 453.32 lei
- 20% Preț: 575.48 lei
- 20% Preț: 585.88 lei
- 20% Preț: 825.93 lei
- 20% Preț: 763.23 lei
- 17% Preț: 360.19 lei
- 20% Preț: 1183.14 lei
- 20% Preț: 340.32 lei
- 20% Preț: 504.57 lei
- 20% Preț: 369.12 lei
- 20% Preț: 583.40 lei
- 20% Preț: 343.62 lei
- 20% Preț: 350.21 lei
- 20% Preț: 764.89 lei
- 20% Preț: 583.40 lei
- Preț: 389.48 lei
- 20% Preț: 341.95 lei
- 20% Preț: 238.01 lei
- 20% Preț: 538.29 lei
Preț: 343.84 lei
Preț vechi: 429.81 lei
-20% Nou
Puncte Express: 516
Preț estimativ în valută:
65.80€ • 68.28$ • 54.100£
65.80€ • 68.28$ • 54.100£
Carte tipărită la comandă
Livrare economică 13-19 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9783540538165
ISBN-10: 354053816X
Pagini: 556
Ilustrații: XI, 541 p.
Dimensiuni: 170 x 242 x 29 mm
Ediția:1991
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 354053816X
Pagini: 556
Ilustrații: XI, 541 p.
Dimensiuni: 170 x 242 x 29 mm
Ediția:1991
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Abstracting background knowledge for concept learning.- A multistrategy learning approach to domain modeling and knowledge acquisition.- Using plausible explanations to bias empirical generalization in weak theory domains.- The replication problem: A constructive induction approach.- Integrating an explanation-based learning mechanism into a general problem-solver.- Analytical negative generalization and empirical negative generalization are not cumulative: A case study.- Evaluating and changing representation in concept acquisition.- Application of empirical discovery in knowledge acquisition.- Using accuracy in scientific discovery.- KBG : A generator of knowledge bases.- On estimating probabilities in tree pruning.- Rule induction with CN2: Some recent improvements.- On changing continuous attributes into ordered discrete attributes.- A method for inductive cost optimization.- When does overfitting decrease prediction accuracy in induced decision trees and rule sets?.- Semi-naive bayesian classifier.- Description contrasting in incremental concept formation.- System FLORA: Learning from time-varying training sets.- Message-based bucket brigade: An algorithm for the apportionment of credit problem.- Acquiring object-knowledge for learning systems.- Learning nonrecursive definitions of relations with linus.- Extending explanation-based generalization by abstraction operators.- Static learning for an adaptative theorem prover.- Explanation-based generalization and constraint propagation with interval labels.- Learning by explanation of failures.- PANEL : Logic and learnability.- Panel on : Causality and learning.- Seed space and version space: Generalizing from approximations.- Integrating EBL with automatic text analysis.- Abduction for explanation-based learning.- Consistent term mappings, term partitions, and inverse resolution.- Learning by analogical replay in prodigy: First results.- Analogical reasoning for logic programming.- Case-based learning of strategicknowledge.- Learning in distributed systems and multi-agent environments.- Learning to relate terms in a multiple agent environment.- Extending learning to multiple agents: Issues and a model for multi-agent machine learning (MA-ML).- Applications of machine learning: Notes from the panel members.- Evaluation of learning systems : An artificial data-based approach.- Shift of bias in learning from drug compounds: The fleming project.- Learning features by experimentation in chess.- Representation and induction of musical structures for computer assisted composition.- IPSA: Inductive protein structure analysis.- Four stances on knowledge acquisition and machine learning.- Programme of EWSL-91.