Machine Learning: An Artificial Intelligence Approach, Volume IIIYves Kodratoff, Ryszard Stanisław Michalski, Jaime Guillermo Carbonell, Tom Michael Mitchell Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment. |
Contents
Explanations Machine Learning | 31 |
JeanGabriel Ganascia | 49 |
PART TWO EMPIRICAL LEARNING METHODS | 61 |
E Stepp | 103 |
An ExemplarBased Learning | 112 |
Probabilistic Decision Trees | 140 |
Integrating Quantitative and Qualitative | 153 |
The Operator | 191 |
PART FOUR INTEGRATED LEARNING SYSTEMS | 397 |
Rendell | 423 |
Guiding Induction with Domain Theories | 474 |
Knowledge Base Refinement as Improving | 493 |
Apprenticeship Learning in Imperfect | 513 |
PART FIVE SUBSYMBOLIC AND HETEROGENOUS | 553 |
GeneticAlgorithmbased Learning | 611 |
Applying Valiants Learning Framework | 641 |
Learning Fault Diagnosis Heuristics from | 214 |
PART THREE ANALYTICAL LEARNING METHODS | 269 |
Acquiring General Iterative Concepts | 302 |
Discovering Algorithms from Weak | 351 |
A System that Learns Using | 360 |
Conditional Operationality | 383 |
A New Approach to Unsupervised Learning | 670 |
Bibliography of Recent Machine Learning Research 19851989 | 685 |
About the Authors | 790 |
| 807 | |
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6th International Workshop applied Artificial Intelligence Artificial Intelligence Approach atom attributes background knowledge BAGGER classification complex Computational Learning Theory Computer Science concept description conceptual clustering Conference on Artificial connectionist construct decision tree defined DeJong described domain knowledge domain theory E₁ equation exemplar experiments expert system explanation Explanation-Based Learning Figure function Genetic Algorithms George Mason University goal heuristic hidden units hypothesis space implementation inductive learning inference rules input vector instance International Conference International Machine Learning Ithaca Knowledge Acquisition knowledge base Knowledge Representation knowledge-based Kodratoff learning procedure learning systems Machine Learning Workshop match Morgan Kaufmann negative examples nodes objects operationality operators output units performance preconditions predicate problem solving Proceedings of IJCAI-89 proof Protos representation San Mateo Schank strategy structure Tecuci theorem tion training examples University values variables version space weights Workshop on Knowledge Workshop on Machine