Results 1  10
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2,250
Maximum Likelihood Linear Transformations for HMMBased Speech Recognition
 COMPUTER SPEECH AND LANGUAGE
, 1998
"... This paper examines the application of linear transformations for speaker and environmental adaptation in an HMMbased speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias ..."
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Cited by 570 (68 self)
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bias, strict linear featurespace transformations are inappropriate in this case. Hence, only modelbased linear transforms are considered. The paper compares the two possible forms of modelbased transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii
An analysis of transformations
 Journal of the Royal Statistical Society. Series B (Methodological
, 1964
"... In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedasti ..."
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Cited by 1067 (3 self)
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In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal
Fast and robust fixedpoint algorithms for independent component analysis
 IEEE TRANS. NEURAL NETW
, 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
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Cited by 884 (34 self)
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Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s
Exploration, normalization, and summaries of high density oligonucleotide array probe level data.
 Biostatistics,
, 2003
"... SUMMARY In this paper we report exploratory analyses of highdensity oligonucleotide array data from the Affymetrix GeneChip R system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of f ..."
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Cited by 854 (33 self)
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analyses of the probe level data motivate a new summary measure that is a robust multiarray average (RMA) of backgroundadjusted, normalized, and logtransformed P M values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance
Multiresolution grayscale and rotation invariant texture classification with local binary patterns
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... This paper presents a theoretically very simple, yet efficient, multiresolution approach to grayscale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain ..."
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Cited by 1299 (39 self)
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transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in true problems of rotation invariance, where the classifier is trained at one particular
Adaptive Sampling With the Ensemble Transform . . .
, 2001
"... A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filt ..."
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Cited by 328 (20 self)
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A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman
Nonlinear spatial normalization using basis functions
 Human Brain Mapping
, 1999
"... Abstract: We describe a comprehensive framework for performing rapid and automatic nonlabelbased nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters to be f ..."
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Cited by 329 (19 self)
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the smoothness of the transformation using a maximum a posteriori (MAP) approach. Most MAP approaches assume that the variance associated with each voxel is already known and that there is no covariance between neighboring voxels. The approach described here attempts to estimate this variance from the data
A transformation for ordering multispectral data in terms of image quality with implications for noise removal
 IEEE Trans. Geosci. Remote Sens
, 1988
"... AbstractAlthough principal components transformations on remotely sensed multispectral data often produce components that show decreasing image quality with increasing component number, there are numerous examples, especially among aircraft scanner data, where this is not the case. This has led us ..."
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Cited by 277 (0 self)
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to define a new trans€ormation, known as the maximum noise fraction (MNF) transformation, which always produces new components ordered by image quality. It can be shown that this transforniation is equivalent to principal components when the noise variance is the same in all bands and that it reduces to a
Mean and Variance Adaptation within the MLLR Framework
 Computer Speech & Language
, 1996
"... One of the key issues for adaptation algorithms is to modify a large number of parameters with only a small amount of adaptation data. Speaker adaptation techniques try to obtain near speaker dependent (SD) performance with only small amounts of speaker specific data, and are often based on initi ..."
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Cited by 145 (15 self)
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Gaussian HMM systems. In this paper MLLR is extended to also update the Gaussian variances and reestimation formulae are derived for these variance transforms. MLLR with variance compensation is evaluated on several large vocabulary recognition tasks. The use of mean and variance MLLR adaptation
A variancestabilizing transformation for geneexpression microarray data
 Bioinformatics
, 2002
"... Motivation: A variance stabilizing transformation for microarray data was recently introduced independently by several research groups. This transformation has sometimes been called the generalized logarithm or glog transformation. In this paper, we derive several alternative approximate variance st ..."
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Cited by 144 (7 self)
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Motivation: A variance stabilizing transformation for microarray data was recently introduced independently by several research groups. This transformation has sometimes been called the generalized logarithm or glog transformation. In this paper, we derive several alternative approximate variance
Results 1  10
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