NDLI: Transfer learning in classification based on semantic analysis

Content Provider	IEEE Xplore Digital Library
Author	Wenlong Lv Weiran Xu Jun Guo
Copyright Year	2012
Description	Author affiliation: Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China (Wenlong Lv; Weiran Xu; Jun Guo)
Abstract	Traditional classification methods, such as supported vector machine and naive bays, rely much on high quality labeled data. However, in many real world applications, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data from related but different domains. In this paper we proposed a novel transfer learning approach to address the classification task while no labeled data available in the target domain and labeled data in related domains available, by selecting features with the similar semantic meanings in both source and target domains. The semantic meanings of words in different domains are extracted through semantic nets built using a complex network model named AF and cosine similarity is used to calculate the semantic similarity of words.
Sponsorship	IEEE Harbin. Sect.
Starting Page	1336
Ending Page	1339
File Size	409539
Page Count	4
File Format	PDF
ISBN	9781467329637
e-ISBN	9781467329644
DOI	10.1109/ICCSNT.2012.6526168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transfer learning Classification Semantic feature
Content Type	Text
Resource Type	Article

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