Investigation on sparse kernel density estimator via harmony data smoothing learning

Hu, Xuelei and Yang, Yingyu (2007). Investigation on sparse kernel density estimator via harmony data smoothing learning. In: Advances in Neural Networks: ISNN 2007. 4th International Symposium on Neural Networks. ISNN 2007: Fourth International Symposium on Neural Networks, Nanjing, China, (1211-1220). 3-7 June, 2007. doi:10.1007/978-3-540-72383-7


Author Hu, Xuelei
Yang, Yingyu
Title of paper Investigation on sparse kernel density estimator via harmony data smoothing learning
Conference name ISNN 2007: Fourth International Symposium on Neural Networks
Conference location Nanjing, China
Conference dates 3-7 June, 2007
Proceedings title Advances in Neural Networks: ISNN 2007. 4th International Symposium on Neural Networks   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2007
Sub-type Fully published paper
DOI 10.1007/978-3-540-72383-7
ISBN 9783540723820
9783540723837
ISSN 0302-9743
1611-3349
Volume 4491
Issue Part 1
Start page 1211
End page 1220
Total pages 10
Language eng
Formatted Abstract/Summary
In this paper we apply harmony data smoothing learning on a weighted kernel density model to obtain a sparse density estimator. We empirically compare this method with the least squares cross-validation (LSCV) method for the classical kernel density estimator. The most remarkable result of our study is that the harmony data smoothing learning method outperforms LSCV method in most cases and the support vectors selected by harmony data smoothing learning method are located in the regions of local highest density of the sample.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Published under "Learning and Approximation".

 
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Created: Mon, 16 Dec 2013, 12:28:54 EST by Xuelei Hu on behalf of School of Information Technol and Elec Engineering