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Cutting Plane Training for Linear Support Vector Machines
May 2013 (vol. 25 no. 5)
pp. 1186-1190
Nicholas A. Arnosti, Stanford University, Palo Alto
Jugal K. Kalita, University of Colorado, Colorado Springs
Support Vector Machines (SVMs) have been shown to achieve high performance on classification tasks across many domains, and a great deal of work has been dedicated to developing computationally efficient training algorithms for linear SVMs. One approach [1] approximately minimizes risk through use of cutting planes, and is improved by [2], [3]. We build upon this work, presenting a modification to the algorithm developed by Franc and Sonnenburg [2]. We demonstrate empirically that our changes can reduce cutting plane training time by up to 40 percent, and discuss how changes in data sets and parameter settings affect the effectiveness of our method.
Index Terms:
Training,Support vector machines,Vectors,Equations,Approximation algorithms,Convergence,Linear approximation,cutting plane SVM,Linear support vector machine
Citation:
Nicholas A. Arnosti, Jugal K. Kalita, "Cutting Plane Training for Linear Support Vector Machines," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 5, pp. 1186-1190, May 2013, doi:10.1109/TKDE.2011.247
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