| Industrial Applications of ML: Illustrations for the KAML Dilemma and the CBR Dream | p. 3 |
| Knowledge Representation in Machine Learning | p. 20 |
| Inverting Implication with Small Training Sets | p. 31 |
| A Context Similarity Measure | p. 49 |
| Incremental Learning of Control Knowledge for Nonlinear Problem Solving | p. 64 |
| Characterizing the Applicability of Classification Algorithms Using Meta-Level Learning | p. 83 |
| Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples | p. 103 |
| FOSSIL : A Robust Relational Learner | p. 122 |
| A Multistrategy Learning System and Its Integration into an Interactive Floorplanning Tool | p. 138 |
| Bottom-Up Induction of Oblivious Read-Once Decision Graphs | p. 154 |
| Estimating Attributes: Analysis and Extensions of RELIEF | p. 171 |
| BMWk Revisited: Generalisation and Formalization of an Algorithm for Detecting Recursive Relations in Term Sequences | p. 183 |
| An Analytic and Empirical Comparison of Two Methods for Discovering Probabilistic Causal Relationships | p. 198 |
| Sample PAC-Learnability in Model Inference | p. 217 |
| Averaging Over Decision Stumps | p. 231 |
| Controlling Constructive Induction in CIPF: An MDL Approach | p. 242 |
| Using Constraints to Building Version Spaces | p. 257 |
| On the Utility of Predicate Invention in Inductive Logic Programming | p. 272 |
| Learning Problem-Solving Concepts by Reflecting on Problem Solving | p. 287 |
| Existence and Nonexistence of Complete Refinement Operators | p. 307 |
| A Hybrid Nearest-Neighbor and Nearest-Hyperrectangle Algorithm | p. 323 |
| Automated Knowledge Acquisition for PROSPECTOR-like Expert Systems | p. 339 |
| On the Role of Machine Learning in Knowledge-Based Control | p. 343 |
| Discovering Dynamics with Genetic Programming | p. 347 |
| A Geometric Approach to Feature Selection | p. 351 |
| Identifying Unrecognizable Regular Languages by Queries | p. 355 |
| Intensional Learning of Logic Programs | p. 359 |
| Partially Isomorphic Generalization and Analogical Reasoning | p. 363 |
| Learning from Recursive, Tree Structured Examples | p. 367 |
| Concept Formation in Complex Domains | p. 371 |
| An Algorithm for Learning Hierarchical Classifiers | p. 375 |
| Learning Belief Network Structure from Data Under Causal Insufficiency | p. 379 |
| Cost-Sensitive Pruning of Decision Trees | p. 383 |
| An Instance-Based Learning Method for Databases: An Information Theoretic Approach | p. 387 |
| Early Screening for Gastric Cancer Using Machine Learning Techniques | p. 391 |
| DP1: Supervised and Unsupervised Clustering | p. 395 |
| Using Machine Learning Techniques to Interpret Results from Discrete Event Simulation | p. 399 |
| Flexible Integration of Multiple Learning Methods into a Problem Solving Architecture | p. 403 |
| Concept Sublattices | p. 407 |
| The Piecewise Linear Classifier DIPOL92 | p. 411 |
| Complexity of Computing Generalized VC-Dimensions | p. 415 |
| Learning Relations Without Closing the World | p. 419 |
| Properties of Inductive Logic Programming in Function-Free Horn Logic | p. 423 |
| Representing Biases for Inductive Logic Programming | p. 427 |
| Biases and Their Effects in Inductive Logic Programming | p. 431 |
| Inductive Learning of Normal Clauses | p. 435 |
| Author Index | p. 439 |
| Table of Contents provided by Blackwell. All Rights Reserved. |
