监督学习的显性与隐性集合覆盖

Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94 Pub Date : 1994-11-06 DOI:10.1109/TAI.1994.346425

Stephen V. Kowalski, D. Moldovan

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引用次数: 0

摘要

研究表明，隐式覆盖算法对于从预分类训练样本中学习概念是有效的。在本文中，我们证明了通过使这些覆盖算法显式，可以学习具有较低错误率的概念。报道了三个实际域的实验结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Explicit versus implicit set-covering for supervised learning

It has been shown that implicit covering algorithms are effective for learning concepts from preclassified training examples. In this paper, we show that by making these covering algorithms explicit, concepts with lower error rates can be learned. Experimental results are reported for three real domains.<>

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Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94

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