We propose an efficient forward regression algorithm based on greedy optimization of marginal likelihood. It can be understood as a forward selection procedure which adds a new bas...
Scaling up document-image classifiers to handle an unlimited variety of document and image types poses serious challenges to conventional trainable classifier technologies. Highly...
Abstract— This paper considers the basis vector selection issue invloved in forward selection algorithms to sparse Gaussian Process Regression (GPR). Firstly, we re-examine a pre...
The interval discrimination task is a classical experimental paradigm that is employed to study working memory and decision making and typically involves four phases. First, the s...
Abstract. We propose an online learning algorithm to tackle the problem of learning under limited computational resources in a teacher-student scenario, over multiple visual cues. ...