Fast Online Training of Ramp Loss Support Vector Machines

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Fast Online Training of Ramp Loss Support Vector Machines
—A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMR s) 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 ConcaveConvex 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...
Zhuang Wang, Slobodan Vucetic
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICDM
Authors Zhuang Wang, Slobodan Vucetic
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