Discovering Simple Rules in Complex Data: A Meta-Learning Algorithm and Some Surprising  Musical Discoveries

Discovering Simple Rules in Complex Data: A Meta-Learning Algorithm and Some Surprising Musical Discoveries (2001)

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by Gerhard Widmer

Venue:	ARTIFICIAL INTELLIGENCE
Citations:	17 - 5 self

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@ARTICLE{Widmer01discoveringsimple,
    author = {Gerhard Widmer},
    title = {Discovering Simple Rules in Complex Data: A Meta-Learning Algorithm and Some Surprising Musical Discoveries},
    journal = {ARTIFICIAL INTELLIGENCE},
    year = {2001},
    volume = {146},
    pages = {129--148}
}

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Abstract

This article presents a new rule discovery algorithm named PLCG that can find simple, robust partial rule models (sets of classification rules) in complex data where it is difficult or impossible to find models that completely account for all the phenomena of interest. Technically speaking,

Citations

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339	Ensemble methods in machine learning - Dietterich - 2000
168	Hierarchical clustering schemes - Johnson
121	Automatic extraction of tempo and beat from expressive performances - Dixon - 2001
118	Separate-and-Conquer Rule Learning - Fürnkranz - 1999
101	Incremental Reduced Error Pruning - Fürnkranz, Widmer - 1994
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42	Efficient pruning methods for separate-and-conquer rule learning systems - Cohen - 1993
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21	Automatic pitch spelling: From numbers to sharps and flats - Cambouropoulos - 2001
20	Using AI and Machine Learning to Study Expressive Music Performance: Project Survey and First Report - Widmer - 2001
12	Large-scale induction of expressive performance rules: First quantitative results - WIDMER - 2000
9	Combining Knowledge-based and Instance-based Learning to Exploit Qualitative Knowledge - Widmer - 1993
5	The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery - Widmer