Learning with a Drifting Target Concept

We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the error rate at a given time, given a learner with access to a history of independent samples labeled according to a target concept that can change on each round. One of our main contributions is a refinement of the best previous results for polynomial-time algorithms for the space of linear separators under a uniform distribution. We also provide general results for an algorithm capable of adapting to a variable rate of drift of the target concept. Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates

Adaptive disk spin-down for mobile computers

We address the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life. One of the most critical resources in mobile computing environments is battery life, and good energy conservation methods increase the utility of mobile systems. We use a simple and efficient algorithm based on machine learning techniques that has excellent performance. Using trace data, the algorithm outperforms several methods that are theoretically optimal under various worst-case assumptions, as well as the best fixed time-out strategy. In particular, the algorithm reduces the power consumption of the disk to about half of the energy consumed by a one minute fixed time-out policy. Furthermore, the algorithm uses as little as 88% of the energy consumed by the best fixed time-out computed in retrospect