Inductive Logic ProgrammingStephen Muggleton Inductive logic programming is a new research area emerging at present. Whilst inheriting various positive characteristics of the parent subjects of logic programming an machine learning, it is hoped that the new area will overcome many of the limitations of its forbears. This book describes the theory, implementations and applications of Inductive Logic Programming. |
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Page 12
Stephen Muggleton. 3.4 PAC - learning One popular machine learning approach to the problem of constructing highly probable hypotheses is the PAC ( Probably Approximately Correct ) model of learning proposed by Valiant [ 14 ] . According ...
Stephen Muggleton. 3.4 PAC - learning One popular machine learning approach to the problem of constructing highly probable hypotheses is the PAC ( Probably Approximately Correct ) model of learning proposed by Valiant [ 14 ] . According ...
Page 114
... PAC - learning ( Probably Approximately Correct learning ) [ 19 , 5 ] , assumes that the examples of the target concept are drawn randomly according to some fixed distribution for learning and for testing the conjecture . It requires ...
... PAC - learning ( Probably Approximately Correct learning ) [ 19 , 5 ] , assumes that the examples of the target concept are drawn randomly according to some fixed distribution for learning and for testing the conjecture . It requires ...
Page 126
... PAC - learning . □ Corollary . k - DNF is PAC - learnable with representative presentations . Proof sketch . Observe that the same algorithm ( except when forming all terms , only those with at most k literals remain ) works for ...
... PAC - learning . □ Corollary . k - DNF is PAC - learnable with representative presentations . Proof sketch . Observe that the same algorithm ( except when forming all terms , only those with at most k literals remain ) works for ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
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0-subsumes abstraction operators applied approach arguments arity Artificial Intelligence background knowledge Buntine C₁ C₂ CIGOL CLINT Closed World Assumption complete Computer concept description constrained atoms constraint predicates constraint theory constructed contains defined definite clauses described domain theory e₁ efficient facts Figure finite first-order first-order logic flattening FOCL FOIL formula framework function symbols given GOLEM ground clause head heuristic Horn clauses hypothesis implied inductive learning Inductive Logic Programming inference input instance instantiation integrity constraints intended interpretation inverse resolution learnable learning algorithms Lemma LINUS literals Machine Learning method Morgan Kaufmann Muggleton multi-valued logic Negation as Failure negative examples non-monotonic logic occur oracle PAC-learning polynomial positive examples problem Prolog proof tree relation representation restricted RLGG rules saturation Section set of clauses Shapiro skolemized sort theory sorted atoms sparky specific subset substitution T₁ target Theorem true truncation tuples unit clauses variables