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13,595
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
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Cited by 555 (22 self)
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toimplement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank. The algorithm is iterative and produces a sequence of matrices {X k, Y k} and at each step, mainly performs a softthresholding operation on the singular values of the matrix Y k. There are two
A Comparative Study on Feature Selection in Text Categorization
, 1997
"... This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI), ..."
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Cited by 1320 (15 self)
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precision) . DF thresholding performed similarly. Indeed we found strong correlations between the DF, IG and CHI values of a term. This suggests that DF thresholding, the simplest method with the lowest cost in computation, can be reliably used instead of IG or CHI when the computation of these measures
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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the convergence the more exact the approximation. • If the hidden nodes are binary, then thresholding the loopy beliefs is guaranteed to give the most probable assignment, even though the numerical value of the beliefs may be incorrect. This result only holds for nodes in the loop. In the maxproduct (or "
Indexing by latent semantic analysis
 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
, 1990
"... A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higherorder structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The p ..."
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Cited by 3779 (35 self)
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are represented as pseudodocument vectors formed from weighted combinations of terms, and documents with suprathreshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.
The mathematics of infectious diseases
 SIAM Review
, 2000
"... Abstract. Many models for the spread of infectious diseases in populations have been analyzed mathematically and applied to specific diseases. Threshold theorems involving the basic reproduction number R0, the contact number σ, and the replacement number R are reviewed for the classic SIR epidemic a ..."
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Cited by 490 (4 self)
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Abstract. Many models for the spread of infectious diseases in populations have been analyzed mathematically and applied to specific diseases. Threshold theorems involving the basic reproduction number R0, the contact number σ, and the replacement number R are reviewed for the classic SIR epidemic
Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex
 J. Neurosci
, 1982
"... The development of stimulus selectivity in the primary sensory cortex of higher vertebrates is considered in a general mathematical framework. A synaptic evolution scheme of a new kind is proposed in which incoming patterns rather than converging afferents compete. The change in the efficacy of a gi ..."
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Cited by 432 (20 self)
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given synapse depends not only on instantaneous pre and postsynaptic activities but also on a slowly varying timeaveraged value of the postsynaptic activity. Assuming an appropriate nonlinear form for this dependence, development of selectivity is obtained under quite general conditions on the sensory
DistortionLimited Vector Quantization
 in Proc.Data Compression Conf.  DCC ’96
, 1996
"... This paper presents a vector quantization system that limits the maximum distortion introduced to a preselected threshold value. This system uses a recently introduced variation of the L1 distortion measure that attempts to minimize the occurrences of quantization errors above a preselected thresho ..."
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Cited by 3 (1 self)
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This paper presents a vector quantization system that limits the maximum distortion introduced to a preselected threshold value. This system uses a recently introduced variation of the L1 distortion measure that attempts to minimize the occurrences of quantization errors above a preselected
Local Maximum Intensity Projection (LMIP): A New Rendering Method for Vascular Visualization
, 1998
"... In order to clearly depict densitometric as well as geometric information in vascular visualization from 3D data such as MR and CT angiographies, a new visualization method called local maximum intensity projection (LMIP) is proposed. LMIP is an extended version of MIP (maximum intensity projection) ..."
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Cited by 13 (0 self)
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). LMIP differs from MIP in that the latter method selects the maximum value along an optical ray, whereas LMIP selects the first local maximum value encountered that is larger than a preselected threshold value along an optical ray from the viewpoint in the viewing direction. Examples are presented
LMIP: Local Maximum Intensity Projection
"... In order to clearly depict densitometric as well as geometric information in vascular visualization from 3D data such as MR and CT angiographies, a new visualization method called local maximum intensity projection (LMIP) is proposed. LMIP is an extended version of MIP (maximum intensity projection) ..."
Abstract
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). LMIP differs from MIP in that the latter method selects the maximum value along an optical ray, whereas LMIP selects the first local maximum value encountered that is larger than a preselected threshold value along an optical ray from the viewpoint in the viewing direction. Examples are presented
Improvements to Platt’s SMO Algorithm for SVM Classifier Design
, 2001
"... This article points out an important source of inefficiency in Platt’s sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO ..."
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Cited by 273 (11 self)
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This article points out an important source of inefficiency in Platt’s sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications
Results 1  10
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13,595