Results 1  10
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6,279
Distance metric learning for large margin nearest neighbor classification
 In NIPS
, 2006
"... We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
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Cited by 695 (14 self)
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We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin
On the algorithmic implementation of multiclass kernelbased vector machines
 Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernelbased 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 ob ..."
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Cited by 559 (13 self)
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In this paper we describe the algorithmic implementation of multiclass kernelbased 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
A discriminatively trained, multiscale, deformable part model
 In IEEE Conference on Computer Vision and Pattern Recognition (CVPR2008
, 2008
"... This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a twofold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge ..."
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Cited by 555 (11 self)
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This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a twofold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007
Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms
 IEEE Transactions on Information Theory
, 2005
"... Important inference problems in statistical physics, computer vision, errorcorrecting 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 t ..."
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Cited by 585 (13 self)
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Important inference problems in statistical physics, computer vision, errorcorrecting 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
On the Evaluation of Marginal Improvements in Pronunciation Variation Modeling for Spanish
"... In the context of large vocabulary speech recognition systems it is of major importance to accurately model the allophonic variations to be faced in a real world task. Evaluation of which variants are actually improving the system performance is crucial, as it determines the acceptance of the pronun ..."
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working or not. Our proposal in this paper is also evaluating the marginal improvement due to every pronunciation variation used (initially restricted to rulebased variant generation), defining specific improvement metrics. We experimentally show how these metrics actually show the improvement achieved
Direct Optimization of Margins Improves Generalization in Combined Classifiers
 Advances in Neural Information Processing Systems
, 1998
"... Sonar Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm. ..."
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Cited by 29 (1 self)
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Sonar Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm.
Paradox lost? Firmlevel evidence on the returns to information systems.
 Manage Sci
, 1996
"... 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 or varie ..."
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Cited by 465 (23 self)
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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
What good are positive emotions
 Review of General Psychology
, 1998
"... This article opens by noting that positive emotions do not fit existing models of emotions. Consequently, a new model is advanced to describe the form and function of a subset of positive emotions, including joy, interest, contentment, and love. This new model posits that these positive emotions ser ..."
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Cited by 454 (15 self)
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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
An algorithm for pronominal anaphora resolution
 Computational Linguistics
, 1994
"... This paper presents an algorithm for identifying the noun phrase antecedents of third person pronouns and lexical anaphors (reflexives and reciprocals). The algorithm applies to the syntactic representations generated by McCord's Slot Grammar parser, and relies on salience measures derived from ..."
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Cited by 391 (0 self)
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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
DISTRIBUTED CONTROL OF TWO DIMENSIONAL VEHICULAR FORMATIONS: STABILITY MARGIN IMPROVEMENT BY MISTUNING
"... We consider distributed control of a large twodimensional (planar) vehicular formation. An individual vehicle in the formation is assumed to be a fully actuated point mass. The control objective is to move the formation with a constant prespecified velocity while maintaining constant intervehicl ..."
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Cited by 5 (5 self)
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to describe the spatiotemporal evolution of velocity perturbations for large number of vehicles, Nveh. The PDE model is used to deduce asymptotic formulae 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
Results 1  10
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6,279