-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.

. The Caltech Mosaic was an experiment to "push the envelope " of multicomputer design and programming toward a system with up to tens of thousands of small, single-chip nodes rather than hundreds of circuit-board-size nodes. The fine-grain multicomputer places more extreme demands

search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing Rank

. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.

speed of learning and/or classification. The basic idea is to use second-derivative information to make a tradeoff between network complexity and training set error. Experiments confirm the usefulness of the methods on a real-world application.

used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [6]. The third contribution is a method for combining classifiers in a “cascade ” which allows

experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black

is based on relative coordinates that are simply the distances from a host to some special network nodes. We propose the second mechanism, called Global Network Positioning (GNP), which is based on absolute coordinates computed from modeling the Internet as a geometric space. Since end hosts maintain

This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second

to be above average for positive events and below average for negative events, ps<.001. Cognitive and motivational considerations led to predictions that degree of desirability, perceived probability, personal experience, perceived controllability, and stereotype salience would influence the amount