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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hamel, L.H. |
| Copyright Year | 2003 |
| Description | Author affiliation: Dept. of Comput. Sci. & Stat., Rhode Island Univ., Kingston, RI, USA (Hamel, L.H.) |
| Abstract | Concept learning is the induction of a description from a set of examples. Inductive logic programming can be considered a special case of the general notion of concept learning specifically referring to the induction of first-order theories. Both concept learning and inductive logic programming can be seen as a search over all possible sentences in some representation language for sentences that correctly explain the examples and also generalize to other sentences that are part of that concept. We explore inductive logic programming with equational logic as the representation language. We present a high-level overview of the implementation of inductive equational logic using genetic programming and discuss encouraging results based on experiments that are intended to emulate real world scenarios. |
| Sponsorship | Air Force Iffice of Sci. Res. Asian Office of Aerospace Res and Development Army Res. Office - Far East CISCO Evoluationary Programming Soc. IEE Inst. of Engineers, Australia IEEE Neural Networks Soc. Univ. of New South Wales |
| Starting Page | 2426 |
| Ending Page | 2433 |
| File Size | 554949 |
| Page Count | 8 |
| File Format | |
| ISBN | 0780378040 |
| DOI | 10.1109/CEC.2003.1299392 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-12-08 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Equations Logic programming Genetic programming Logic functions Software testing Computer science Statistics Robustness Algebra Computer languages |
| Content Type | Text |
| Resource Type | Article |
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