NDLI: Fast Online Training of Ramp Loss Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Zhuang Wang Vucetic, S.
Copyright Year	2009
Abstract	A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMRs) is proposed. It finds the optimal SVMR for t+1 training examples using SVMR built on t previous examples. The algorithm retains the Karush–Kuhn–Tucker conditions on all previously observed examples. This is achieved by an SMO-style incremental learning and decremental unlearning under the Concave-Convex Procedure framework. Further speedup of training time could be achieved by dropping the requirement of optimality. A variant, called OnlineASVMR, is a greedy approach that approximately optimizes the SVMR objective function and is suitable for online active learning. The proposed algorithms were comprehensively evaluated on 9 large benchmark data sets. The results demonstrate that OnlineSVMR (1) has the similar computational cost as its offline counterpart; (2) outperforms IDSVM, its competing online algorithm that uses hinge-loss, in terms of accuracy, model sparsity and training time. The experiments on online active learning show that for a fixed number of label queries OnlineASVMR (1) achieves consistently better accuracy than QueryAll and competitive accuracy to Greedy approach; (2) outperforms the active learning version of IDSVM.
Starting Page	569
Ending Page	577
File Size	496917
Page Count	9
File Format	PDF
ISBN	9781424452422
ISSN	15504786
DOI	10.1109/ICDM.2009.53
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Cost function Machine learning Fasteners Training data Large-scale systems Machine learning algorithms Data mining USA Councils Computational efficiency active learning SVM CCCP SMO ramp loss online learning
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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