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 385
... domain theories that are incomplete and incorrect performs less work and produces more accurate concepts than FOCL learning with no domain theory ; * an incomplete and incorrect domain theory allows FOCL to tolerate classification noise ...
... domain theories that are incomplete and incorrect performs less work and produces more accurate concepts than FOCL learning with no domain theory ; * an incomplete and incorrect domain theory allows FOCL to tolerate classification noise ...
Page 386
... domain theory and FOCL with no domain theory , and recorded the accuracy of the concept learned by each version ( calculated by testing on 500 positive and 500 negative examples ) and the number of times the information gain of a ...
... domain theory and FOCL with no domain theory , and recorded the accuracy of the concept learned by each version ( calculated by testing on 500 positive and 500 negative examples ) and the number of times the information gain of a ...
Page 390
... domain the- ory errors . The empirical method is needed when no subset of the possible operationalizations of the domain theory will result in a correct hypothesis . 4.2 Learning from noisy data with incomplete and incorrect do- main ...
... domain the- ory errors . The empirical method is needed when no subset of the possible operationalizations of the domain theory will result in a correct hypothesis . 4.2 Learning from noisy data with incomplete and incorrect do- main ...
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