[1]许敏,俞林.一种新颖的领域自适应概率密度估计器[J].智能系统学报,2015,10(02):221-226.[doi:10.3969/j.issn.1673-4785.201312041]
 XU Min,YU Lin.A probability density estimator for domain adaptation[J].CAAI Transactions on Intelligent Systems,2015,10(02):221-226.[doi:10.3969/j.issn.1673-4785.201312041]
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一种新颖的领域自适应概率密度估计器(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年02期
页码:
221-226
栏目:
出版日期:
2015-04-25

文章信息/Info

Title:
A probability density estimator for domain adaptation
作者:
许敏12 俞林2
1. 江南大学 数字媒体学院, 江苏 无锡 214122;
2. 无锡职业技术学院 物联网技术学院, 江苏 无锡 214121
Author(s):
XU Min12 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
关键词:
概率密度函数无偏置 v-SVR中心约束最小包含球核心集领域自适应
Keywords:
probability density estimationno bias v-SVRcenter-constrained minimum enclosing ball(CC-MEB)core setdomain adaptation
分类号:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201312041
文献标志码:
A
摘要:
传统概率密度估计法建立好密度估计模型后,无法将源域知识传递给相关目标域密度估计模型。提出用无偏置 v-SVR 的回归函数来表示传统概率密度估计法获得密度估计信息,并说明无偏置 v-SVR 等价于中心约束最小包含球及概率密度回归函数可由中心约束最小包含球中心点表示。在上述理论基础上提出中心点知识传递领域自适应概率密度估计法,用于解决因目标域信息不足而无法建立概率密度函数的场景。实验表明,此种领域自适应方法进行领域间知识传递的同时,还能达到源域隐私保护的目的。
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.

参考文献/References:

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[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.

备注/Memo

备注/Memo:
收稿日期:2013-12-20;改回日期:。
基金项目:江苏省高校自然科学研究资助项目(13KJB520001);江苏省高校哲学社会科学基金资助项目(2012SJB880077);江苏省研究生创新工程资助项目(CXZZ12-0759).
作者简介:许敏:女,1980年生,讲师,博士,主要研究方向为模式识别、人工智能。
通讯作者:许敏.E-mail:xum@wxit.edu.cn.
更新日期/Last Update: 2015-06-15