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Using Polynomial Approximations to Discover Qualitative Models
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gerceker, Reha Kamil |
| Copyright Year | 2006 |
| Abstract | Automating the discovery of qualitative models from observations is a difficult problem of machine learning and various algorithms have been proposed for the solution of this problem in the literature. In this paper, we present a new algorithm called LYQUID, which uses polynomials fitted on observed numerical data as approximations to the underlying real world functions; constraint discovery is then performed over those polynomials. LYQUID is shown to be a fast and successful learning algorithm even in the presence of a high |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.dartmouth.edu/~cbk/qr06/papers/gerceker-say-qr06.pdf |
| Alternate Webpage(s) | http://www.cs.dartmouth.edu/~qr06/papers/gerceker-say-qr06.pdf |
| Alternate Webpage(s) | http://www.cmpe.boun.edu.tr/graduate/allthesis/m_2006_GercekerReha%20Kamil.pdf |
| Alternate Webpage(s) | http://www.qrg.northwestern.edu/papers/Files/qr-workshops/QR06/gerceker-say-qr06.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
