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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shchekotykhin, K. Friedrich, G. |
| Copyright Year | 2009 |
| Abstract | Efficient acquisition of constraint networks is a key factor for the applicability of constraint problem solving methods. Current techniques learn constraint networks from sets of training examples, where each example is classified as either a solution or non-solution of a target network. However, in addition to this classification, an expert can usually provide arguments as to why examples should be rejected or accepted. Generally speaking domain specialists have partial knowledge about the theory to be acquired which can be exploited for knowledge acquisition. Based on this observation, we discuss the various types of arguments an expert can formulate and develop a knowledge acquisition algorithm for processing these types of arguments which gives the expert the possibility to input arguments in addition to the learning examples. The result of this approach is a significant reduction in the number of examples which must be provided to the learner in order to learn the target constraint network. |
| Starting Page | 476 |
| Ending Page | 482 |
| File Size | 521219 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424452422 |
| ISSN | 15504786 |
| DOI | 10.1109/ICDM.2009.62 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-12-06 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Knowledge acquisition Intelligent systems Intelligent networks Informatics Constraint theory Vocabulary Data mining Problem-solving Knowledge based systems Recommender systems knowledge acquisition argumentation constrains |
| Content Type | Text |
| Resource Type | Article |
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