自动化学报Issue(6)：1176-1183,8.DOI:10.3724/SP.J.1004.2014.01176
一种面向多源领域的实例迁移学习
Instance-based Transfer Learning for Multi-source Domains
摘要
Abstract
The most remarkable characteristic of transfer learning is that it can employ the knowledge in relative domains to help perform the learning tasks in the domain of the target. With the use of different fields of knowledge for target task learning, transfer learning can transfer and share the information between similar domains or tasks, making the traditional learning from scratch an addable one, which implies that the learning effciency is higher and the cost is lower. For the specific situation that the shared knowledge in the domains of the source and the target are sample data with similar distribution, an instance transfer learning method based on multi-sources dynamic TrAdaBoost is put forward. Integrated with the knowledge in multiple source domains, this method makes the target task learning the one that is able to make good use of the information of all source domains. Whenever candidate classifiers are trained, all the samples in all source domains are involved in learning, and the information conducive to target task learning can be obtained, so that negative transfer can be avoided. The theoretical analysis suggests that the given algorithm is better than the single source transfer. By means of adding the dynamic factor, this algorithm improves the defect that weight entropy drifts from source to target instances. The experimental results support that the given algorithm has the advantage of improving the recognition rate.关键词
多源/TrAdaBoost/实例迁移/迁移学习Key words
Multi-source/TrAdaBoost/instance transfer/transfer learning引用本文复制引用
张倩,李明,王雪松,程玉虎,朱美强..一种面向多源领域的实例迁移学习[J].自动化学报,2014,(6):1176-1183,8.基金项目
国家自然科学基金(61072094,61273143),江苏省自然科学基金(BK 20130207),教育部新世纪优秀人才支持计划(NCET-10-0765),教育部高等学校博士学科点专项科研基金(20110095110016,20120095110025)资助@@@@Supported by National Natural Science Foundation of China (61072094,61273143), Natural Science Foundation of Jiangsu Province (BK20130207), Program for New Century Excellent Talents in University (NCET-10-0765), and Specialized Research Fund for the Doctoral Program of Higher Education of China (20110095110016,20120095110025) （61072094,61273143）
