Machine Learning - EWSL-91: European Working Session on Learning, Porto, Portugal, March 6-8, 1991. ProceedingsYves Kodratoff In this book contemporary knowledge of superconductivity is set against its historical background. First, the highlights of superconductivity research in the twentieth century are reviewed. Further contributions then describe the basic phenomena resulting from the macroscopic quantum state of superconductivity (such as zero resistivity, the Meissner-Ochsenfeld effect, and flux quantization) and review possible mechaniscs, including the classical BCS theory and the more recent alternative theories. The main categories of superconductors - elements, intermetallic phases, chalcogenides, oxides and organic compounds - are described. Common features and differences in their structure and electronic properties are pointed out. This broad overview of superconductivity is completed by a discussion of properties related to the coherence length. Newcomers to the field who seek an overall picture of research in superconductivity, and of the cross-links between its branches, will find this volume especially useful. |
Contents
ABSTRACTING BACKGROUND KNOWLEDGE FOR CONCEPT LEARNING | 1 |
A MULTISTRATEGY LEARNING APPROACH TO DOMAIN MODELING AND KNOWLEDGE ACQUISITION | 14 |
Using Plausible Explanations to Bias Empirical Generalization in Weak Theory Domains | 33 |
A CONSTRUCTIVE INDUCTION APPROACH | 44 |
INTEGRATING AN EXPLANATIONBASED LEARNING MECHANISM INTO A GENERAL PROBLEMSOLVER | 62 |
A CASE STUDY | 81 |
Evaluating and Changing Representation in Concept Acquisition | 89 |
Application of Empirical Discovery in Knowledge Acquisition | 101 |
EXPLANATIONBASED GENERALIZATION AND CONSTRAINT PROPAGATION WITH INTERVAL LABELS | 312 |
Learning By Explanation of Failures | 327 |
Logic and Learnability | 344 |
CAUSALITY AND LEARNING | 345 |
GENERALIZING FROM APPROXIMATIONS | 346 |
INTEGRATING EBL WITH AUTOMATIC TEXT ANALYSIS | 347 |
ABDUCTION for EXPLANATIONBASED LEARNING | 348 |
CONSISTENT TERM MAPPINGS TERM PARTITIONS AND INVERSE RESOLUTION | 361 |
USING ACCURACY IN SCIENTIFIC DISCOVERY | 118 |
A GENERATOR OF KNOWLEDGE BASES | 137 |
ON ESTIMATING PROBABILITIES IN TREE PRUNING | 138 |
Some Recent Improvements | 151 |
ON CHANGING CONTINUOUS ATTRIBUTES INTO ORDERED DISCRETE ATTRIBUTES | 164 |
A Method for Inductive Cost Optimization | 179 |
When Does Overfitting Decrease Prediction Accuracy in Induced Decision Trees and Rule Sets? | 192 |
SEMINAIVE BAYESIAN CLASSIFIER | 206 |
DESCRIPTION CONTRASTING in INCREMENTAL CONCEPT FORMATION | 220 |
Learning from TimeVarying Training Sets | 234 |
AN ALGORITHM FOR THE APPORTIONMENT OF CREDIT PROBLEM | 235 |
Acquiring ObjectKnowledge for Learning Systems | 245 |
LEARNING NONRECURSIVE DEFINITIONS OF RELATIONS WITH LINUS | 265 |
Extending ExplanationBased Generalization by Abstraction Operators | 282 |
STATIC LEARNING FOR AN ADAPTATIVE THEOREM PROVER | 298 |
FIRST RESULTS | 375 |
Analogical Reasoning for Logic Programming | 391 |
CASEBASED LEARNING OF STRATEGIC KNOWLEDGE | 398 |
Learning in Distributed Systems and MultiAgent Environments | 412 |
Learning to Relate Terms in a Multiple Agent Environment | 424 |
Issues and a Model for MultiAgent Machine Learning MAML | 440 |
Notes from the Panel Members | 457 |
AN ARTIFICIAL DATABASED APPROACH | 463 |
THE FLEMING PROJECT | 482 |
Learning Features by Experimentation in Chess | 494 |
REPRESENTATION AND INDUCTION OF MUSICAL STRUCTURES FOR COMPUTER ASSISTED COMPOSITION | 512 |
| 513 | |
FOUR STANCES ON KNOWLEDGE ACQUISITION AND MACHINE LEARNING | 514 |
PROGRAMME of EWSL91 | 534 |
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Common terms and phrases
abduction abstraction accuracy ADECLU/S agents algorithm analogical application approach arguments arity Artificial Intelligence attributes background knowledge BOLERO Bratko Carbonell classifier clause CLP(R Computer concept description conjunction constraint propagation construct data sets DCFringe decision tree defined definition described descriptors discretisation domain model domain theory EBGF evaluation expert system explanation Explanation-Based Learning fact FAHRENHEIT Figure formula function generalisation given goal heuristic Horn clauses hypothesis induction initial input instance instantiated integration IS-A knowledge acquisition knowledge base knowledge representation Kodratoff learned rules learning system LINUS logic lymphography Machine Learning method Michalski Morgan Kaufmann Muggleton multi-agent multi-agent systems negative examples node noise object operators overfitting parameters partition performance possible predicate problem solving Proceedings Prolog proof pruning Quinlan relations replication represent representation solution solver specific step strategy subgoals subset target concept Tecuci theorem training set values variables version space