Maximum Margin Classification on Convex Euclidean Metric Spaces

Summary. In this paper, we present a new implementable learning algorithm for the general nonlinear binary classification problem. The suggested algorithm abides the maximum margin philosophy, and learns a decision function from the set of all finite linear combinations of continuous differentiable basis functions. This enables the use of a much more flexible function class than the one usually employed by Mercer-restricted kernel machines. Experiments on 2-dimensional randomly generated data are given to compare the algorithm to a Support Vector Machine. While the performances are comparable in case of Gaussian basis functions and static feature vectors the algorithm opens a novel way to hitherto intractable problems. This includes especially classification of feature vector streams, or features with dynamically varying dimensions as such in DNA analysis, natural speech or motion image recognition. 1 Overcoming Mercer conditions In the late seventies, Vapnik and Chervonenkis [1] had introduced the concept of a maximum margin separating hyperplane. It has been shown, tha

Machine learning with Lipschitz classifiers

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010André Stuhlsat