Machine Learning - EWSL-91

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.