OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method

Abstract. We present the OnlineDoubleMaxMinOver approach to obtain the Support Vectors in two class classification problems. With its linear time complexity and linear convergence the algorithm achieves a competitive speed. We approach the problem of the impossibility of perfect non trivial online Support Vector Learning by parameterising the exactness. Even in the case of linearly inseparable data within the feature space the method converges to a solution expressible by a finite amount of information while observing an arbitrarily large number of input vectors. The results of the online method are comparable to the batch ones, occasionally even better.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Daniel Schneegaß, Thomas Martinetz, Michael Clausohm
• Keywords: Class Classification Problems, ESANN 2006, ESANN 2007, Linear Time Complexity, Support Vector