We study hierarchical classification in the general case when an instance could belong to more than one class node in the underlying taxonomy. Experiments done in previous work sh...
We propose a novel cost-efficient approach to threshold selection for binary web-page classification problems with imbalanced class distributions. In many binary-classification ta...
Abstract. This paper deals with the classification of color video sequences using Markov Random Fields (MRF) taking into account motion information. The theoretical framework relie...
Interactive clustering refers to situations in which a human labeler is willing to assist a learning algorithm in automatically clustering items. We present a related but somewhat...
Sumit Basu, Danyel Fisher, Steven M. Drucker, Hao ...
We provide a new analysis of an efficient margin-based algorithm for selective sampling in classification problems. Using the so-called Tsybakov low noise condition to parametrize...