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Abstract: An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they ... (Update)
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.... et al. 34] Splice sites in DNA Pattern matching Wang et al. 31] Markov chain Salzberg [26] Promoters in DNA Neural networks Opitz et al. [21] Decision tree Hirsh et al. 11] Protein classification rules Hidden Markov model Krogh et al. 13] Neural networks Wu et al. 33]...
.... machine learning in domains containing significant numerical components has previously been accomplished by using neural networks [12]. This is because of problems in encoding numeric properties in the logic programming context. One solution is the utilisation of...
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BibTeX entry: (Update)
D. W. Opitz and J. W. Shavlik, "Connectionist theory refinement: Genetically searching the space of network topologies," Journal of Artificial Intelligence Research, vol. 6, pp. 177--209, 1997. http://citeseer.ist.psu.edu/opitz97connectionist.html More
@article{ opitz97connectionist,
author = "David Opitz and Jude W. Shavlik",
title = "Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies",
journal = "Journal of Artificial Intelligence Research",
volume = "6",
pages = "177--209",
year = "1997",
url = "citeseer.ist.psu.edu/opitz97connectionist.html" }
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