In this work we consider the problem of binary classification where the classifier may abstain instead of classifying each observation, leaving the critical items for human evaluat...
Estimating the error rates of classifiers or regression models is a fundamental task in machine learning which has thus far been studied exclusively using supervised learning tech...
Pinar Donmez, Guy Lebanon, Krishnakumar Balasubram...
There are many clustering tasks which are closely related in the real world, e.g. clustering the web pages of different universities. However, existing clustering approaches neglec...
Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven t...
Abstract. This paper proposes a novel method to deal with the representation issue in texture classification. A learning framework of image descriptor is designed based on the Fish...