Data classification with a relaxed model of variable kernel density estimation

Yen Jen Oyang, Yu Yen Ou, Shien Ching Hwang, Chien Yu Chen, Darby Tien Hau Chang

研究成果: Conference contribution

9 引文 斯高帕斯(Scopus)

摘要

In recent years, kernel density estimation has been exploited by computer scientists to model several important problems in machine learning, bioinformatics, and computer vision. However, in case the dimension of the data set is high, then the conventional kernel density estimators suffer poor convergence rates of the pointwise mean square error (MSE) and the integrated mean square error (IMSE). Therefore, design of a novel kernel density estimator that overcomes this problem has been a great challenge for many years. This paper proposes a relaxed model of the variable kernel density estimation and analyzes its performance in data classification applications. It is proved in this paper that, in terms of pointwise MSE, the convergence rate of the relaxed variable kernel density estimator can approach O(n-1) regardless of the dimension of the data set, where n is the number of sampling instances. Experiments with the data classification applications have shown that the improved convergence rate of the pointwise MSE leads to higher prediction accuracy. In fact, the experimental results have also shown that the data classifier constructed based on the relaxed variable kernel density estimator is capable of delivering the same level of prediction accuracy as the SVM with the Gaussian kernel.

原文English
主出版物標題Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
頁面2831-2836
頁數6
DOIs
出版狀態Published - 2005
事件International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
持續時間: 2005 七月 312005 八月 4

出版系列

名字Proceedings of the International Joint Conference on Neural Networks
5

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
國家/地區Canada
城市Montreal, QC
期間05-07-3105-08-04

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人工智慧

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