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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 1999: Methodologies for Knowledge Discovery and Data Mining pp 238–246Cite as

Neural Network Based Classifiers for a Vast Amount of Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

Abstract

When using neural networks to train a large number of data for classification, there generally exists a learning complexity problem. In this paper, a new geometrical interpretation of McCulloch-Pitts (M–P) neural model is presented. Based on the interpretation, a new constructive learning approach is discussed. Experimental results show that the new algorithm can greatly reduce the learning complexity and can be applied to real classification problems with a vast amount of data.

Supported by National Nature Science Foundation & National Basic Research Program of China

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Author information

Authors and Affiliations

  1. The State Key Lab of Intelligent Technology and Systems Dept. of Computer Science, Tsinghua University, Beijing, China

    Ling Zhang

  2. The State Key Lab of Intelligent Technology and Systems Artificial Intelligence Institute, Anhui University, Anhui, China

    Bo Zhang

Authors
  1. Ling Zhang
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  2. Bo Zhang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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Zhang, L., Zhang, B. (1999). Neural Network Based Classifiers for a Vast Amount of Data. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_32

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  • DOI: https://doi.org/10.1007/3-540-48912-6_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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