NDLI: A weighted algorithm of inductive transfer learning based on maximum entropy model

Content Provider	IEEE Xplore Digital Library
Author	Yuhong Zhang Xuegang Hu Canhua Mei Peipei Li
Copyright Year	2010
Description	Author affiliation: Department of Computer Science and Technology, Hefei University of Technology, China (Yuhong Zhang; Xuegang Hu; Canhua Mei; Peipei Li)
Abstract	Traditional machine learning and data mining algorithms mainly assume that the training and test data must be in the same feature space and follow the same distribution. However, in real applications, these two hypotheses are difficult to hold, traditional algorithms are hence no longer applicable. As a new framework of learning, transfer learning could solve this problem effectively. This paper focuses on one of important branches in this field, namely inductive transfer learning. Correspondingly, a weighted algorithm of inductive transfer learning, based on maximum entropy model, is proposed, called WTLME. It transfers the model parameters learned from the source domain to the target domain, and meanwhile adjusts the weights of instances in the target domain to obtain the model with high accuracy. Extensive studies demonstrate that our proposed algorithm of WTLME is more effective and efficient than traditional algorithms that require learning from scratch if the data distributions change. Moreover, WTLME is comparable to the previous transfer algorithm based on maximum entropy model.
Starting Page	2740
Ending Page	2745
File Size	277654
Page Count	6
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569513
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	transfer learning Machine learning algorithms Error analysis Adaptation model data mining Machine learning maximum entropy Entropy Data models Classification algorithms machine learning
Content Type	Text
Resource Type	Article

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