Machine Learning: An Integrated Framework and Its Applications
This exploration of machine-learning concepts provides a general framework which describes in a unified manner the different learning techniques, including the top-down inductive generation of discriminant rules and explanation-based learning.
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RELATING LOGICAL EXPRESSIONS
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algorithm angle(x application arch arity Artificial Intelligence attributes background knowledge basic Bergadano car 1 wheel Chapter classes classification rules complete concept descriptions consists constraints constructor contains correct corresponding counterexamples curve(x database decision trees deductive defined definition described discriminant domain theory eobj(x European Working Sessions evaluation example components example f expert system Explanation-Based Learning extensional function Giordana heuristic Horn clauses hypotheses implemented Incremental inductive instance knowledge base knowledge representation Kodratoff language learning process learning set learning system LEGO logical formulas Machine Learning mbrick mechanalysis MEPS methodology ML-SMART negation node objects obtained ontop ontop(x,y operational predicates performed phase possible predicate sets problem Proc procedure production rules quantifiers recognition rate relational algebra representation Saitta set HD shape shape'(x sp-graph specialization tree specific statistical criteria strategy structured structured domains subproblem graph Table trailer tuples University of Torino variables vector wheelbarrow