In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
We present a framework for learning features for visual discrimination. The learning system is exposed to a sequence of training images. Whenever it fails to recognize a visual co...
The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While...
Web application testers need automated, effective approaches to validate the test results of complex, evolving web applications. In previous work, we developed a suite of automate...
Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the train...