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Statistics for Experimenters
, 2005
"... R factor = 0.052; wR factor = 0.114; datatoparameter ratio = 18.4. The title compound, [Zn(C8H10F3O2)2(CH4O)2], is a dimethanol coordinated zinc complex with the acetyl acetonate derivative 1,1,1trifluoro5,5dimethylhexane2,4dionate. The bisdiketonate complex, which is isostructural with its ..."
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Cited by 675 (1 self)
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Co analogue, is located on a crystallographic inversion center. The complex is octahedral with basically no distortion, and the methanol molecules are in trans positions with respect to one another. The planes of thediketonate and the ZnO4 unit are tilted by 18.64 (10) against each other. O—H O
Active Learning with Statistical Models
, 1995
"... For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative, statist ..."
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Cited by 678 (10 self)
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For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative
Additive Logistic Regression: a Statistical View of Boosting
 Annals of Statistics
, 1998
"... Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input dat ..."
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Cited by 1750 (25 self)
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Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input
A review of image denoising algorithms, with a new one
 SIMUL
, 2005
"... The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding perf ..."
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Cited by 509 (6 self)
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The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding
Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics
, 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
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Cited by 542 (13 self)
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For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain
Thresholding of statistical maps in functional neuroimaging using the false discovery rate.
 NeuroImage
, 2002
"... Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a longstanding problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for mult ..."
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Cited by 520 (9 self)
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Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a longstanding problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures
High confidence visual recognition of persons by a test of statistical independence
 IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1993
"... A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a sample of the ..."
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Cited by 620 (8 self)
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A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a sample
A statistical interpretation of term specificity and its application in retrieval
 Journal of Documentation
, 1972
"... Abstract: The exhaustivity of document descriptions and the specificity of index terms are usually regarded as independent. It is suggested that specificity should be interpreted statistically, as a function of term use rather than of term meaning. The effects on retrieval of variations in term spec ..."
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Cited by 586 (3 self)
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Abstract: The exhaustivity of document descriptions and the specificity of index terms are usually regarded as independent. It is suggested that specificity should be interpreted statistically, as a function of term use rather than of term meaning. The effects on retrieval of variations in term
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
, 1998
"... This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I err ..."
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Cited by 723 (8 self)
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This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I
The Dantzig selector: statistical estimation when p is much larger than n
, 2005
"... In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n ≪ ..."
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Cited by 875 (14 self)
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In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n
Results 1  10
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2,926,977