Learning theory and programs to date are inductively bounded: they can be described as "wind-up toys" which can only learn the kinds of things that their designers envisi...
Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection o...
Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can bene ...
On the evening of 2 November 1988, someone “infected” the Internet with a worm program. That program exploited flaws in utility programs in systems based on BSD-derived versi...
Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with se...