David P. Helmbold

A dynamic disk spin-down technique for mobile computing

We address the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life. Since one of the most critical resources in mobile computing environments is battery life, good energy conservation methods can dramatically increase the utility of mobile systems. We use a simple and efcient algorithm based on machine learning techniques that has excellent performance in practice. Our experimental results are based on traces collected from HP C2474s disks. Using this data, the algorithm outperforms several algorithms that are theoretically optimal in under various worst-case assumptions, as well as the best xed time-out strategy. In particular, the algorithm reduces the power consumption of the disk to about half (depending on the disks properties) of the energy consumed by a one minute xed time-out. Since the algorithm adapts to usage patterns, it uses as little as 88% of the energy consumed by the best xed time-out computed in retrospect.

Tracking Drifting Concepts By Minimizing Disagreements

AbstractIn this paper we consider the problem of tracking a subset of a domain (called the target) which changes gradually over time. A single (unknown) probability distribution over the domain is used to generate random examples for the learning algorithm and measure the speed at which the target changes. Clearly, the more rapidly the target moves, the harder it is for the algorithm to maintain a good approximation of the target. Therefore we evaluate algorithms based on how much movement of the target can be tolerated between examples while predicting with accuracy ε Furthermore, the complexity of the class

Predicting nearly as well as the best pruning of a decision tree

\mathcal{H}

Learning nested differences of intersection-closed concept classes

of possible targets, as measured by d, its VC-dimension, also effects the difficulty of tracking the target concept. We show that if the problem of minimizing the number of disagreements with a sample from among concepts in a class

Learning Permutations with Exponential Weights

\mathcal{H}

On Weak Learning

can be approximated to within a factor k, then there is a simple tracking algorithm for