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 482
... experiments in detail . 4.1 Experiment 1 : comparator faults In this experiment the faulty component was the comparator of the array switching regulator . Induction experiments were carried out on three training sets ; each included a ...
... experiments in detail . 4.1 Experiment 1 : comparator faults In this experiment the faulty component was the comparator of the array switching regulator . Induction experiments were carried out on three training sets ; each included a ...
Page 508
... experiments were performed . In the first series , five sets of clauses were induced , one for each of the small training sets ( 100 examples ) , and their classification accuracy was tested on the 5000 examples from the large sets . In ...
... experiments were performed . In the first series , five sets of clauses were induced , one for each of the small training sets ( 100 examples ) , and their classification accuracy was tested on the 5000 examples from the large sets . In ...
Page 510
... Experiments To examine the effects of noise , three series of experiments were conducted , introducing noise in the training examples in three different ways . First , noise was added in the values of the arguments of the target ...
... Experiments To examine the effects of noise , three series of experiments were conducted , introducing noise in the training examples in three different ways . First , noise was added in the values of the arguments of the target ...
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
3 | 21 |
Copyright | |
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Common terms and phrases
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