NDLI: Adapting SVM Classifiers to Data with Shifted Distributions

Content Provider	IEEE Xplore Digital Library
Author	Yang, Jun Yan, Rong Hauptmann, Alexander G.
Copyright Year	2007
Abstract	Many data mining applications can benefit from adapt- ing existing classifiers to new data with shifted distribu- tions. In this paper, we present Adaptive Support Vector Machine (Adapt-SVM) as an efficient model for adapting a SVM classifier trained from one dataset to a new dataset where only limited labeled examples are available. By in- troducing a new regularizer into SVM's objective function, Adapt-SVM aims to minimize both the classification error over the training examples, and the discrepancy between the adapted and original classifier. We also propose a selective sampling strategy based on the loss minimization principle to seed the most informative examples for classifier adap- tation. Experiments on an artificial classification task and on a benchmark video classification task shows that Adapt- SVM outperforms several baseline methods in terms of ac- curacy and/or efficiency.
Starting Page	69
Ending Page	76
File Size	427196
Page Count	8
File Format	PDF
ISBN	9780769530192
DOI	10.1109/ICDMW.2007.37
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Learning systems Conferences Support vector machine classification Drives Streaming media Sampling methods Data mining Application software
Content Type	Text
Resource Type	Article

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