Issue No.05 - May (2013 vol.25)
Nicholas A. Arnosti , Stanford University, Palo Alto
Jugal K. Kalita , University of Colorado, Colorado Springs
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.247
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  approximately minimizes risk through use of cutting planes, and is improved by , . We build upon this work, presenting a modification to the algorithm developed by Franc and Sonnenburg . 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.
Training, Support vector machines, Vectors, Equations, Approximation algorithms, Convergence, Linear approximation, cutting plane SVM, Linear support vector machine
Nicholas A. Arnosti, Jugal K. Kalita, "Cutting Plane Training for Linear Support Vector Machines", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 5, pp. 1186-1190, May 2013, doi:10.1109/TKDE.2011.247