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Abstract: Most researchers in machine learning have built their learning systems under the assumption that some external entity would do all the work of furnishing the learning experiences. Recently, however, investigators in several subfields of machine learning have designed systems that play an active role in choosing the situations from which they will learn. Such activity is generally called exploration. This paper describes a few of these exploratory learning projects, as reported in the... (Update)
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BibTeX entry: (Update)
@misc{ frank-exploration,
author = "Michael P. Frank",
title = "Exploration in Machine Learning",
url = "citeseer.ist.psu.edu/frank90exploration.html" }
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15 Learning by experimentation: Acquiring and refining problem-.. (context) - Mitchaell, Utgoff
11 Heuristics for empirical discovery (context) - Langley, Simon et al.
10 Rule creation and rule learning through environmental explor.. (context) - Shen, Simon
8 The ubiquity of discovery (context) - Lenat
6 Learning novel domains through curiosity and conjecture - Scott, Markovitch
3 Inductive learning from preclassified training examples (context) - Shavlik, Dietterich
2 Exploratory learning in the game of go: Initial results (context) - Pell - 1990
1 A comparative analysis of some simple architectures for auto.. (context) - Genesereth - 1988
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