Geometric methods are very intuitive and provide a theoretically solid viewpoint to many optimization problems. SVM is a typical optimization task that has attracted a lot of atte...
Michael E. Mavroforakis, Margaritis Sdralis, Sergi...
We develop an intuitive geometric interpretation of the standard support vector machine (SVM) for classification of both linearly separable and inseparable data and provide a rigo...
This paper introduces a geometrically inspired large-margin classifier that can be a better alternative to the Support Vector Machines (SVMs) for the classification problems with ...
We introduce a novel method for face recognition from image sets. In our setting each test and training example is a set of images of an individual’s face, not just a single ima...
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. ...
Francis R. Bach, Gert R. G. Lanckriet, Michael I. ...