This paper proposes a novel framework for automatic text categorization problem based on the kernel density classifier. The overall goal is to tackle two main issues in automatic ...
Dwi Sianto Mansjur, Ted S. Wada, Biing-Hwang Juang
Abstract We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking un...
In active learning, one attempts to maximize classifier performance for a given number of labeled training points by allowing the active learning algorithm to choose which points...
Active Learning (AL) is a selective sampling strategy which has been shown to be particularly cost-efficient by drastically reducing the amount of training data to be manually ann...
Abstract. In some learning settings, the cost of acquiring features for classification must be paid up front, before the classifier is evaluated. In this paper, we introduce the fo...
Jason V. Davis, Jungwoo Ha, Christopher J. Rossbac...