论文信息 - An efficient multiple predicate learner

An efficient multiple predicate learner

In this paper, we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a single predicate learning module CORE. A fast failure mechanism is also proposed which contributes learning effiency and learnability to the algorithm. MPL-CORE employs background knowledge that can be represented in intensional (Horn clauses) or extensional (ground atoms) forms during its learning process. With the fast failure mechanism, MPL-CORE outperforms previous multiple predicate learning systems in both the computational complexity and learnability.

Masayuki Numao | Xiaolong Zhang

[1] Stephen Muggleton,et al. Efficient Induction of Logic Programs , 1990, ALT.

[2] Masayuki Numao,et al. Efficient Learning of Logic Programs with Non-determinant, Non-discriminating Literals , 1991, ML.

[3] Masayuki Numao,et al. Efficient Multiple Predicate Learner Based on Fast Failure Mechanism , 1996, PRICAI.

[4] R. Ichise,et al. Inductive Logic Programming and Genetic Programming , 1998, ECAI.

[5] Marie desJardins,et al. Evaluation and selection of biases in machine learning , 1995, Machine Learning.

[6] Michael J. Pazzani,et al. Relational Clichés: Constraining Induction During Relational Learning , 1991, ML.

[7] Leon Sterling,et al. The Art of Prolog , 1987, IEEE Expert.

[8] Masayuki Numao,et al. Discrimination-Based Constructive Induction of Logic Programs , 1992, AAAI.

[9] Saso Dzeroski,et al. Inductive Logic Programming: Techniques and Applications , 1993 .

[10] Raymond J. Mooney,et al. Automated refinement of first-order horn-clause domain theories , 2005, Machine Learning.

[11] Luc De Raedt,et al. Multiple Predicate Learning , 1993, IJCAI.

[12] J. R. Quinlan. Learning Logical Definitions from Relations , 1990 .

[13] Raymond J. Mooney,et al. Automated refinement of first-order horn-clause domain theories , 2005, Machine Learning.