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Proceedings Paper

Transfer estimation and the applications in data stream classification
Author(s): Zhihao Zhang; Jie Zhou
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Paper Abstract

Transfer estimation is a new parameter estimation method, which utilizes samples from not only the target distribution but also related ones. It is based on traditional estimators and can be used to solve the problem of small sample estimation. The key problem in transfer estimation is how to set the weighting coefficients when designing the transfer estimators. In this paper, we will propose a new algorithm to solve this problem. First, we will propose a common rule. Following the proposed rule, we can formulate the weighting coefficient setting problem as a constrained optimization problem. We will introduce an alternating optimized method and get a new algorithm of transfer estimation. The estimation of evolving class priors in data stream classification is a typical and important small sample estimation problem. In this paper, we will apply the proposed transfer estimation algorithm to the class prior estimation. Experiments on benchmark data sets will be performed, which show that the proposed algorithm can improve the performance on both class prior estimation and the final data stream classification.

Paper Details

Date Published: 2 December 2011
PDF: 7 pages
Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 800402 (2 December 2011); doi: 10.1117/12.900341
Show Author Affiliations
Zhihao Zhang, Tsinghua Univ. (China)
Jie Zhou, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 8004:
MIPPR 2011: Pattern Recognition and Computer Vision
Jonathan Roberts; Jie Ma, Editor(s)

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