Machine Learning: An Artificial Intelligence Approach, Volume IIRyszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell |
Contents
Challenges of | 27 |
PART TWO LEARNING CONCEPTS AND RULES FROM | 43 |
Learning to Predict Sequences | 63 |
Copyright | |
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abstract acquisition algorithm analogy applied Artificial Intelligence associated attributes BACON bias Carnegie-Mellon University causal chunking classifier Cognitive components Computer Science concept description concept learning conceptual clustering condition constraint construct DALTON decision tree defined derived described description language descriptors discovery disjunction domain E-function E₁ Eleusis encoding equation example experience expert systems facts figure formulation function GEN-NODE genetic algorithm given GLAUBER goal has-quality object heuristics hierarchy hypotheses inductive inference inference rules Infimum initial instantiation involved is_on knowledge laws learner Machine Learning mapping match memory method nodes operator outputs parameters possible preconditions predicates problem solving procedure production protohistories R. S. Michalski reaction reacts inputs reasoning relation representation rule satisfied schema schemata sequence similar situation solution specific STAHL step structure subgoal substances symbol T. M. Mitchell Eds task tion transformation University values variables version space