Karen A. Glocer

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Algorithm 1: SoftBoost 1. Input: S = 〈(x1, y1), . . . , (xN , yN )〉, desired accuracy δ, and capping parameter ν ∈ [1, N ]. 2. Initialize: dn to the uniform distribution 3.Do for t = 1, . . . (a) Train classifier on dt−1 and {u1, . . . ,ut−1} and obtain hypothesis ht. Set un = h(xn)yn. (b) Calculate the edge γt of ht : γt = dt · ut (c) Set γ̂t = (minm=1...t(More)
Online feature selection (OFS) provides an efficient way to sort through a large space of features, particularly in a scenario where the feature space is large and features take a significant amount of memory to store. Image processing operators, and especially combinations of image processing operators, provide a rich space of potential features for use in(More)
Access latency to secondary storage devices is frequently a limiting factor in computer system performance. New storage technologies promise to provide greater storage densities at lower latencies that is currently obtainable with hard disk drives. MEMS-based storage devices [1, 11, 20] use orthogonal magnetic or physical recording techniques and thousands(More)
Many file migration algorithms rely on simple, unchanging, automated heuristics to make file placement decisions for exclusively hierarchical storage systems. Such approaches cannot adapt to changes in the workload or data center configuration. Systems with manually-tuned policies offer a way to deal with changes but require well-trained administrators,(More)
The behavior of firms has been described as predatory, and sometimes it seems that nothing but a suit and tie separates Wall Street from the wild. Economics is rife with biological analogies, but it may be that the analogy works both ways. Perhaps it is as valid to describe the behavior of predators in terms of microeconomic firms as it is to call firms(More)
Feature selection can be defined as the problem of finding the optimal subset of features that meet some criterion, but in general it is an ill-posed problem. In practice, the only way to compare feature selection algorithms has been through generalization error. For this reason it is extremely rare to see optimality discussed in the context of feature(More)
In recent years much progress has been made on computer gameplay in games of complete information such as chess and go. Computers have surpassed the ability of top chess players and are well on their way to doing so at Go. Games of incomplete information, on the other hand, are far less studied. Despite significant financial incentives, computerized poker(More)
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