Lecture notes in computer science, 482.; Lecture notes in computer science., Lecture notes in artificial intelligence.
CONTENTS NOTE
Text of Note
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 strategic knowledge --; 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.