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Abstract: The aim of Closed Loop Machine Learning (CLML) is to partially automate some aspects of scientific work, namely the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. We have developed ASE-Progol (part of our CLML system) which uses ILP to construct hypothesised first-order theories and uses a CART-like algorithm to select... (Update)
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BibTeX entry: (Update)
C. H. Bryant and S. H. Muggleton. Closed loop machine learning. Technical Report YCS 330, University of York, Department of Computer Science, Heslington, York, YO10 5DD, UK., 2000. http://citeseer.ist.psu.edu/bryant00closed.html More
@misc{ bryant00closed,
author = "C. Bryant and S. Muggleton",
title = "Closed loop machine learning",
text = "C. H. Bryant and S. H. Muggleton. Closed loop machine learning. Technical
Report YCS 330, University of York, Department of Computer Science, Heslington,
York, YO10 5DD, UK., 2000.",
year = "2000",
url = "citeseer.ist.psu.edu/bryant00closed.html" }
Citations (may not include all citations):1447 The mathematical theory of communication (context) - Shannon, Weaver - 1949
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96 Drug design by machine learning: The use of inductive logic .. (context) - King, Muggleton et al. - 1992
94 Constructing optimal binary decision trees is NP-complete (context) - Hyal, Rivest - 1976
91 Improving generalization with active learning - Cohn, Atlas et al. - 1994
83 Query by committee - Seung, Opper et al. - 1992
81 Sequential Analysis (context) - Wald - 1947
[Article contains additional citations not shown here]
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