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On avoiding redundancy in inductive logic programming systems (2003).
| Content Provider | CiteSeerX |
|---|---|
| Author | Fonseca, Nuno Costa, Vitor S. Silva, Fernando Camacho, Rui |
| Abstract | Inductive Logic Programming (ILP) is a subfield of Machine Learning that provides an excellent framework for learning in multi-relational domains and inducing first-order clausal theories. ILP systems perform a search through very large hypothesis spaces containing redundant hypotheses. The generation of redundant hypotheses may prevent the systems from finding good models and increase the time to induce them. In this paper we propose a classification of hypotheses redundancy. We show how expert knowledge can be provided to an ILP system to avoid the generation of redundant hypotheses. Preliminary results suggest that the the number of hypotheses generated and execution time are substancially reduced when using expert knowledge to avoid the generation of redundant hypotheses. |
| File Format | |
| Publisher Date | 2003-01-01 |
| Access Restriction | Open |
| Subject Keyword | Redundant Hypothesis Inductive Logic Programming System Ilp System First-order Clausal Theory Excellent Framework Machine Learning Inductive Logic Programming Hypothesis Redundancy Multi-relational Domain Execution Time Expert Knowledge Large Hypothesis Space Good Model Preliminary Result |
| Content Type | Text |
| Resource Type | Article |
