[1]WANG Xiaochu,BAO Fang,WANG Shitong,et al.Transfer learning classification algorithms based on minimax probability machine[J].CAAI Transactions on Intelligent Systems,2016,11(1):84-92.[doi:10.11992/tis.201505024]
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Transfer learning classification algorithms based on minimax probability machine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 1 Page number: 84-92 Column: 学术论文—机器学习 Public date: 2016-02-25
Title:
Transfer learning classification algorithms based on minimax probability machine
Author(s):
WANG Xiaochu1; 2;  BAO Fang2; 3;  WANG Shitong1;  XU Xiaolong1
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. Information Fusion Software Engineering Research and Development Center of Jiangsu Province, Jiangyin Pdyteehnie College, Jiangyin 214405, China;
3. Department of Computer Science, Jiangyin Pdyteehnie College, Jiangyin 214405, China
Keywords:
transfer learning; minimax probability machine; classification; source domain; target domain; regularization
CLC:
TP391.4
DOI:
10.11992/tis.201505024
Abstract:
Traditional transfer learning classification algorithms solve related (but not identical) data classification issues by using a large number of labeled samples in the source domain and small amounts of labeled samples in the target domain. However, this technique does not apply to the transfer learning of data from different categories of learned source domain data. To solve this problem, we constructed a transfer learning constraint term using the source domain data and the limited labeled data in the target domain to generate a regularized constraint for the minimax probability machine. We propose a transfer learning classification algorithm based on the minimax probability machine known as TL-MPM. Experimental results on 20 Newsgroups data sets demonstrate that the proposed algorithm has higher classification accuracy for small amounts of target domain data. Therefore, we confirm the effectiveness of the proposed algorithm.
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