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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12976 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11807 (17 self)
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situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
Factor Analysis for Background Suppression
 International Conference on Pattern Recognition
, 2002
"... Factor analysis (FA) is a statistical technique similar to principal component analysis (PCA) for explaining the variance in a data set in terms of underlying linear factors. Unlike PCA, however, FA has not been widely exploited for face or object recognition. This paper explains the differences bet ..."
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Cited by 6 (3 self)
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Factor analysis (FA) is a statistical technique similar to principal component analysis (PCA) for explaining the variance in a data set in terms of underlying linear factors. Unlike PCA, however, FA has not been widely exploited for face or object recognition. This paper explains the differences
Nonparametric model for background subtraction
 in ECCV ’00
, 2000
"... Abstract. Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel nonparametric background model and a background subtraction approach. The model can ..."
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Cited by 538 (17 self)
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Abstract. Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel nonparametric background model and a background subtraction approach. The model
Type IIB GreenSchwarz superstring in plane wave RamondRamond background
 Nucl. Phys. B
"... We construct the covariant κsymmetric superstring action for type IIB superstring on plane wave space supported by RamondRamond background. The action is defined as a 2d sigmamodel on the coset superspace. We fix the fermionic and bosonic lightcone gauges in the covariant GreenSchwarz superstri ..."
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Cited by 476 (0 self)
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We construct the covariant κsymmetric superstring action for type IIB superstring on plane wave space supported by RamondRamond background. The action is defined as a 2d sigmamodel on the coset superspace. We fix the fermionic and bosonic lightcone gauges in the covariant Green
Convex Analysis
, 1970
"... In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a lo ..."
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Cited by 5350 (67 self)
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In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a
Bayes Factors
, 1995
"... In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null ..."
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Cited by 1766 (74 self)
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In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null
Wallflower: Principles and Practice of Background Maintenance
, 1999
"... Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a threecomponent system for background maintenance: the pixellevel component performs Wiener filtering to make probabilistic predictions of the expected background; the regionlevel component fills i ..."
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Cited by 468 (1 self)
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Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a threecomponent system for background maintenance: the pixellevel component performs Wiener filtering to make probabilistic predictions of the expected background; the regionlevel component fills
Determining the Number of Factors in Approximate Factor Models
, 2000
"... In this paper we develop some statistical theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose a panel Cp criterion and show that the number of factors c ..."
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Cited by 538 (29 self)
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of the number of factors for configurations of the panel data encountered in practice. The idea that variations in a large number of economic variables can be modelled bya small number of reference variables is appealing and is used in manyeconomic analysis. In the finance literature, the arbitrage pricing
Directional Statistics and Shape Analysis
, 1995
"... There have been various developments in shape analysis in the last decade. We describe here some relationships of shape analysis with directional statistics. For shape, rotations are to be integrated out or to be optimized over whilst they are the basis for directional statistics. However, various c ..."
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Cited by 775 (31 self)
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concepts are connected. In particular, certain distributions of directional statistics have emerged in shape analysis, such a distribution is Complex Bingham Distribution. This paper first gives some background to shape analysis and then it goes on to directional distributions and their applications
Results 1  10
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