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Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1996
"... We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and c ..."
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Cited by 774 (20 self)
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We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum
Empirical Bayes Analysis of a Microarray Experiment
 Journal of the American Statistical Association
, 2001
"... Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. We consider one such experiment in whi ..."
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Cited by 492 (20 self)
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simultaneous inferences concerning which genes were affected by the radiation. Although our focus is on one speci � c experiment, the proposed methods can be applied quite generally. The empirical Bayes inferences are closely related to the frequentist false discovery rate (FDR) criterion. 1.
Learning Bayesian belief networks: An approach based on the MDL principle
 Computational Intelligence
, 1994
"... A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's Minimal Description Length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being lear ..."
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Cited by 254 (7 self)
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be conceptually and computationally intractable. In such a case it would be preferable to use a simpler model even if it is less accurate. The MDL principle o ers a reasoned method for making this tradeo. We also show that our method generalizes previous approaches based on Kullback crossentropy. Experiments
Region Competition and its Analysis: A Unified Theory for Image Segmentation
, 1995
"... We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and c ..."
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Cited by 2 (0 self)
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We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum
Unsupervised Word Induction Using Mdl Criterion
 IN PROCEEDINGS ISCSL2000
, 2000
"... Unsupervised learning of units (phonemes, words, phrases, etc.) is important to the design of statistical speech and NLP systems. This paper presents a general sourcecoding framework for inducing words from natural language text without word boundaries. An efficient search algorithm is developed to ..."
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Cited by 6 (0 self)
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to optimize the minimum description length (MDL) induction criterion. Despite some seemingly oversimplified modeling assumption, we achieved good results on several word induction problems.
Suboptimal behavior of Bayes and MDL in classification under misspecification
 COLT
, 2004
"... We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means that there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative to the Bayesian ..."
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Cited by 19 (4 self)
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We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means that there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative
APPLICATION OF MDL CRITERION FOR MICROWAVE IMAGING BY MUSIC ALGORITHM
"... Abstract—Multiple signal classification (MUSIC) algorithm has been applied to localize small scatterers for superresolution imaging. A problem associated with this application is the estimation of the number of scatterers in presence of noise and multiple scattering between targets. In this paper, ..."
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are independent, the nearby wave sources are grouped together to improve the independency criterion. The application of MDL to synthetic and experimental data verifies accurate estimation of the target number with low complexity, even if the data embodies significant noise and multiple scattering. 1.
PACMDL bounds
, 2003
"... We point out that a number of standard sample complexity bounds (VCdimension, PACBayes, and others) are all related to the number of bits required to communicate the labels given the unlabeled data for a natural communication game. Motivated by this observation, we give a general sample comple ..."
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Cited by 25 (1 self)
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We point out that a number of standard sample complexity bounds (VCdimension, PACBayes, and others) are all related to the number of bits required to communicate the labels given the unlabeled data for a natural communication game. Motivated by this observation, we give a general sample
Suboptimal Behavior of Bayes and MDL in Classification under Misspecification
"... Abstract. We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative to the Baye ..."
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Abstract. We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative
MDL Based Fitness Functions for Learning
"... Genetic programming starts with an initial population of computer programs composed of elementary functions and termir_als ~9, 10, 8]. Genetic operators, such as crossover and selection, are used to adapt the shape and size of the programs and evolve increasingly fit populations. This process can b ..."
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Genetic programming starts with an initial population of computer programs composed of elementary functions and termir_als ~9, 10, 8]. Genetic operators, such as crossover and selection, are used to adapt the shape and size of the programs and evolve increasingly fit populations. This process can be viewed as a search for a highly fit computer program, Abe•t, in the whole program space
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