[1]XU Min,YU Lin.A probability density estimator for domain adaptation[J].CAAI Transactions on Intelligent Systems,2015,10(2):221-226.[doi:10.3969/j.issn.1673-4785.201312041]
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A probability density estimator for domain adaptation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 2 Page number: 221-226 Column: 学术论文—人工智能基础 Public date: 2015-04-25
Title:
A probability density estimator for domain adaptation
Author(s):
XU Min1; 2;  YU Lin2
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. School of Internet of Things Technology, Wuxi Institute of Technology, Wuxi 214121, China
Keywords:
probability density estimation; no bias v-SVR; center-constrained minimum enclosing ball(CC-MEB); core set; domain adaptation
CLC:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201312041
Abstract:
This paper proposes that the density information received from the traditional probability density estimation method can be represented by no bias v-SVR regression function. It addresses the problem that after the source domain’s probability density estimation model is established using the traditional probability density estimation method its source domain knowledge can not be transferred to the relevant target domain’s density estimation model. In this paper, no bias v-SVR is equivalent to the center-constrained minimum enclosing ball (CC-MEB) and the probability density regression function is constrained by CC-MEB’s center point is described. On the basis of the above theory, an adaptive probability density evaluation method for transferring knowledge through the center point was put forward to solve the problem that an accurate probability density estimation model can not be established because of the lack of information of the target domain. The experiments showed that this adaptive method can reach the goals of knowledge transfer between domains and privacy protection in the source domain.
References:
[1] VAPNIK V N. Statistical learning theory [M]. New York:John Wiley and Sons, 1998: 35-41.
[2] 吉根林, 姚瑶. 一种分布式隐私保护的密度聚类算法[J].智能系统学报, 2009, 4(2):137-141.JI Genlin, YAO Yao. Density-based privacy preserving distributed clustering algorithm[J]. CAAI Transactions on Intelligent Systems, 2009, 4(2):137-141.
[3] PARZEN E. On estimation of a probability density function and mode[J]. The Annals of Mathematical Statistics, 1962, 33(3): 1065-1076.
[4] GIROLAMI M, HE C. Probability density estimation from optimally condensed data samples[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25 (10): 1253-1264.
[5] DENG Z H, CHUNG F L, WANG S T. FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation[J]. Pattern Recognition, 2008, 41(4):1363-1372.
[6] TSANG I W, KWOK J T, ZURADA J M. Generalized core vector machines[J]. IEEE Transactions on Neural Networks, 2006, 17(5): 1126-1140.
[7] TSANG I W, KWOK J T, CHEUNG P M. Core vector machines: fast SVM training on very large data sets[J]. Journal of Machine Learning Research, 2005(6): 363-392.
[8] CHU C S, TSANG I W, KWOK J K. Scaling up support vector data description by using core-sets[C]//IEEE International Joint Conference on Neural Networks. Budapest, Hungary: 2004: 425-430.
[9] 许敏,王士同. 基于最小包含球的大数据集域自适应快速算法[J]. 模式识别与人工智能, 2013, 26(2): 159-168.XU Min, WANG Shitong. A fast learning algorithm based on minimum enclosing ball for large domain adaptation[J]. Pattern Recognition and Artificial Intelligence, 2013, 26(2): 159-168.
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