We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin

In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic

This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007

Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems

working or not. Our proposal in this paper is also evaluating the marginal improvement due to every pronunciation variation used (initially restricted to rule-based variant generation), defining specific improvement metrics. We experimentally show how these metrics actually show the improvement achieved

Sonar Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm.

T he "productivity paradox" of information systems (IS) is that, despite enormous improvements in the underlying technology, the benefits of IS spending have not been found in aggregate output statistics.One explanation is that IS spending may lead to increases in product quality

in theory building and hypothesis needs more studies on positive emotions, not testing. In so doing, psychologists have inadver- simply to level the uneven knowledge bases tently marginalized the emotions, such as joy, between negative and positive emotions, but interest, contentment, and love, that share a

information concerning semantic and real world relations to the algorithm's decision procedure. Interestingly, this enhancement only marginally improves the algorithm's performance (by 2%). The algorithm is compared with other approaches to anaphora resolution which have been proposed

to describe the spatio-temporal evolution of velocity perturbations for large number of vehicles, Nveh. The PDE model is used to deduce asymptotic for-mulae for the stability margin (least stable eigenvalue). We show that the stability margin of the closed loop decays to 0 as the number of vehicles increases