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首页> 外文会议>12th European Conference on Machine Learning, 12th, Sep 5-7, 2001, Freiburg, Germany >Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy
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Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy

机译：通过PLCG规则学习策略发现表现力音乐表现的强力原则

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摘要

We present a new rule learning algorithm named PLCG ― a kind of ensemble learning method ― that can find simple, robust partial theories (sets of classification rules) in complex data where neither high coverage nor high precision can be expected. The motivating application problem comes from an interdisciplinary research project that aims at discovering fundamental principles of expressive music performance from large amounts of complex real-world data (measurements of actual performances by concert pianists). It is shown that PLCG succeeds in finding some surprisingly simple and robust performance principles, some of which represent truly novel and musically meaningful discoveries. A more systematic experiment shows that PLCG learns significantly simpler theories than more direct approaches to rule learning, while striking a compromise between coverage and precision.
机译：我们提出了一种新的规则学习算法，称为PLCG（一种整体学习方法），它可以在无法期望高覆盖范围和高精度的复杂数据中找到简单，鲁棒的局部理论（分类规则集）。激励应用的问题来自一个跨学科的研究项目，该项目旨在从大量复杂的现实世界数据（音乐会钢琴演奏家对实际表演的测量）中发现表现音乐表演的基本原理。结果表明，PLCG成功地找到了一些令人惊讶的简单而强大的性能原理，其中一些代表了真正新颖和音乐上有意义的发现。一项更系统的实验表明，与更直接的规则学习方法相比，PLCG学到的理论要简单得多，同时要在覆盖率和精度之间做出折衷。

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