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145
The minimum error minimax probability machine
 Journal of Machine Learning Research
, 2004
"... We construct a distributionfree Bayes optimal classifier called the Minimum Error Minimax Probability Machine (MEMPM) in a worstcase setting, i.e., under all possible choices of classconditional densities with a given mean and covariance matrix. By assuming no specific distributions for the data, ..."
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Cited by 21 (7 self)
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We construct a distributionfree Bayes optimal classifier called the Minimum Error Minimax Probability Machine (MEMPM) in a worstcase setting, i.e., under all possible choices of classconditional densities with a given mean and covariance matrix. By assuming no specific distributions for the data
MINIMUM BAYES ERROR FEATURE SELECTION
"... ABSTRACT We consider the problem of designing a linear transformation ` 2 IRp\Theta n, of rank p ^ n, which projects the features of a classifier x 2 IRn onto y = `x 2 IRp such as to achieve minimum Bayes error (or probability of misclassification). Two avenues will be explored: the first is to maxi ..."
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ABSTRACT We consider the problem of designing a linear transformation ` 2 IRp\Theta n, of rank p ^ n, which projects the features of a classifier x 2 IRn onto y = `x 2 IRp such as to achieve minimum Bayes error (or probability of misclassification). Two avenues will be explored: the first
Random Projections for Support Vector Machines
"... Let X be a data matrix of rank ρ, representing n points in ddimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input mat ..."
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Cited by 12 (3 self)
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Let X be a data matrix of rank ρ, representing n points in ddimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input
Support vector machines in face recognition with occlusions
 in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition
"... Support Vector Machines (SVM) are one of the most useful techniques in classification problems. One clear example is face recognition. However, SVM cannot be applied when the feature vectors defining our samples have missing entries. This is clearly the case in face recognition when occlusions are p ..."
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Cited by 27 (0 self)
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criterion which minimizes the probability of overlap. The resulting optimization problem can be solved efficiently and we show how the global minimum of the error term is guaranteed under mild conditions. We provide extensive experimental results, demonstrating the superiority of the proposed approach over
An inversion algorithm to compute blocking probabilities in loss networks with statedependent rates
 IEEE/ACM Trans. Networking
, 1995
"... Abstract — We extend our recently developed algorithm for computing (exact) steadystate blocking probabilities for each class in productform loss networks to cover general statedependent arrival and service rates. This generalization allows us to consider, for the first time, a wide variety of bu ..."
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Cited by 20 (7 self)
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reduction by elimination of nonbinding resources and by conditional decomposition based on special structure, an effective scaling algorithm to control errors in the inversion, efficient treatment of multiple classes with identical parameters and truncation of large sums. We show that the computational
1ReducedDimension Multiuser Detection
"... We present a reduceddimension multiuser detector (RDMUD) structure that significantly decreases the number of required correlation branches at the receiver frontend, while still achieving performance similar to that of the conventional matchedfilter (MF) bank. RDMUD exploits the fact that the n ..."
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waveforms, which in turn are chosen from a large class of spreading codes. We derive the probabilityoferror when using two methods for recovery of active users and their transmitted symbols: the reduceddimension decorrelating (RDD) detector, which combines subspace projection and thresholding
Dimension Independent Matrix Square using MapReduce
, 2011
"... We compute the singular values of an m×n tall and skinny (m ≫ n) sparse matrix A without dependence on m, for very large m. In particular, we give a simple nonadaptive sampling scheme where the singular values of A are estimated within relative error with high probability. Our proven bounds focus on ..."
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Cited by 3 (1 self)
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We compute the singular values of an m×n tall and skinny (m ≫ n) sparse matrix A without dependence on m, for very large m. In particular, we give a simple nonadaptive sampling scheme where the singular values of A are estimated within relative error with high probability. Our proven bounds focus
Minimum Variance Estimation of a Sparse Vector Within the Linear Gaussian Model: An
"... Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterizat ..."
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Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a
Model reduction and system identification for master equation control systems
 In: Proc. of the American Control Conference, IEEE Press, Piscataway
, 2003
"... Abstract A master equation describes the continuoustime evolution of a probability distribution, and is characterized by a simple bilinear structure and an oftenhigh dimension. We develop a model reduction approach in which the number of possible configurations and corresponding dimension is redu ..."
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Cited by 2 (0 self)
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Abstract A master equation describes the continuoustime evolution of a probability distribution, and is characterized by a simple bilinear structure and an oftenhigh dimension. We develop a model reduction approach in which the number of possible configurations and corresponding dimension
An Improved Threshold Ring Signature Scheme Based on Error Correcting Codes
"... Abstract. The concept of threshold ring signature in codebased cryptography was introduced by Aguilar et al. in [1]. Their proposal uses Stern’s identification scheme as basis. In this paper we construct a novel threshold ring signature scheme built on the qSD identification scheme recently propos ..."
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proposed by Cayrel et al. in [14]. Our proposed scheme benefits of a performance gain as a result of the reduction in the soundness error from 2/3 for Stern’s scheme to 1/2 per round for the qSD scheme. Our threshold ring signature scheme uses random linear codes over the field Fq, secure in the random
Results 1  10
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145