This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce versio...
Learning to rank is becoming an increasingly popular research area in machine learning. The ranking problem aims to induce an ordering or preference relations among a set of insta...
We propose a low cost method for the correction of the output of OCR engines through the use of human labor. The method employs an error estimator neural network that learns to as...
Abstract. Active Learning methods rely on static strategies for sampling unlabeled point(s). These strategies range from uncertainty sampling and density estimation to multi-factor...
This paper addresses the problem of concept sampling. In many real-world applications, a large collection of mixed concepts is available for decision making. However, the collecti...