Machine Learning: An Artificial Intelligence Approach, Volume IIRyszard Stanisław Michalski, Jaime G. Carbonell, Tom M. Mitchell |
Contents
Chapter | 2 |
Eighties | 27 |
PART | 43 |
Chapter 4 | 63 |
Chapter 5 | 69 |
Paul E Utgoff | 107 |
Chapter 6 | 149 |
Improving the Generalization Step | 215 |
Chapter 7 | 412 |
Program Synthesis as a Theory Formation | 419 |
PART FIVE LEARNING BY OBSERVATION AND | 423 |
Inventing | 471 |
An Approach to Learning from Observation | 571 |
PART SIX AN EXPLORATION OF GENERAL ASPECTS | 591 |
Learning Control | 647 |
| 669 | |
PART THREE COGNITIVE ASPECTS OF LEARNING | 245 |
The General | 285 |
Toward | 311 |
PART FOUR LEARNING BY ANALOGY | 349 |
A Theory | 371 |
Programming by Analogy | 393 |
| 671 | |
Updated Glossary of Selected Terms in Machine Learning | 707 |
About the Authors | 715 |
| 725 | |
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Common terms and phrases
abstract acid acquisition algorithm applied approach Artificial Intelligence attributes BACON bias Carbonell CARL Carnegie-Mellon University causal corpus chunking classifier Cognitive components concept conceptual clustering condition consider constraint construct DALTON defined depth-first search described description language developed discovery discovery system disjunction domain E₁ E₂ encoding example experience expert systems facts Figure formulation function genetic algorithm Gentner given GLAUBER has-quality object heuristics hypotheses inductive inference Infimum initial instance instantiation involved knowledge laws learner loop Machine Learning mapping mechanism memory method NaOH operator outputs oxygen parameters phlogiston theory possible power law preconditions predicates problem solving production protohistories R. S. Michalski reaction reacts inputs relation representation rule schema sequence similar situation solution space specific STAHL step structural matching subgoal subset substances symbol task theory tion transformation universally quantified values variables version space