International Conference on Computing: Theory and Applications (ICCTA'07) Fast Single-Shot Multiclass Proximal Support Vector Machines and Perceptions Kolkata, India March 05-March 07 ISBN: 0-7695-2770-1
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCTA.2007.60
Recently Sandor Szedmak and John Shawe-Taylor [1] showed that Multiclass Support Vector Machines [3, 4] can be implemented with single class complexity. In this paper we show that computational complexity of their algorithm can be further reduced by modelling the problem as a Multiclass Proximal Support Vector Machines. The new formulation requires only a linear equation solver. The paper then discusses the multiclass transformation of Iterative Single data Algorithm [8]. This method is faster than the first method. The two algorithm are so much simple that SVM training and testing of huge datasets can be implemented even in a spreadsheet.
Citation:
K.P. Soman, R. Loganathan, M.S. Vijaya, V. Ajay, K. Shivsubramani, "Fast Single-Shot Multiclass Proximal Support Vector Machines and Perceptions," iccta, pp.294-298, International Conference on Computing: Theory and Applications (ICCTA'07), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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