Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
By considering a new metric, Nikov and Nikova defined the class of error-set correcting codes. These codes differ from the errorcorrecting codes in the sense that the minimum dis...
In this paper we present a scalable and distributed access structure for similarity search in metric spaces. The approach is based on the Content– addressable Network (CAN) parad...
Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other task...
Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show how to introduce prior knowledge into linear supp...