Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to le...
We study the problem of learning a kernel which minimizes a regularization error functional such as that used in regularization networks or support vector machines. We consider thi...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
The problem of model selection for support vector machines (SVMs) is considered. We propose an evolutionary approach to determine multiple SVM hyperparameters: The covariance matr...
We consider parameterized convex optimization problems over the unit simplex, that depend on one parameter. We provide a simple and efficient scheme for maintaining an -approximat...
We present a new method for transductive learning, which can be seen as a transductive version of the k nearest-neighbor classifier. Unlike for many other transductive learning me...