The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
We describe a new boosting algorithm that is the first such algorithm to be both smooth and adaptive. These two features make possible performance improvements for many learning ...
Systems are increasingly expected to adapt themselves to changing requirements and environmental situations with minimum user interactions. A challenge for self-adaptation is the i...
—As wireless devices and sensors are increasingly deployed on people, researchers have begun to focus on wireless body-area networks. Applications of wireless body sensor network...
Gang Zhou, Jian Lu, Chieh-Yih Wan, Mark D. Yarvis,...
We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for t...