Learning different types of new attributes by combining the neural network and iterative attribute construction

Yuh-Jyh Hu*

*Corresponding author for this work

研究成果: Conference contribution同行評審

摘要

Most of the current constructive induction algorithms degrade performance as the target concept becomes larger and more complex in terms of Boolean combinations. Most are only capable of constructing relatively smaller new attributes. Though it is impossible to build a learner to learn any arbitrarily large and complex concept, there are some large and complex concepts that could be represented in a simple relation such as prototypical concepts, e.g., m-of-n, majority, etc. In this paper, we propose a new approach that combines the neural net and iterative attribute construction to learn relatively short but complex Boolean combinations and prototypical structures. We also carried a series of systematic experiments to characterize our approach.

原文English
主出版物標題Machine Learning
主出版物子標題ECML-97 - 9th European Conference on Machine Learning, Proceedings
編輯Maarten van Someren, Gerhard Widmer, Gerhard Widmer
發行者Springer Verlag
頁面124-137
頁數14
ISBN(列印)3540628584, 9783540628583
DOIs
出版狀態Published - 1 一月 1997
事件9th European Conference on Machine Learning, ECML 1997 - Prague, Czech Republic
持續時間: 23 四月 199725 四月 1997

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
1224
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference9th European Conference on Machine Learning, ECML 1997
國家Czech Republic
城市Prague
期間23/04/9725/04/97

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