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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 11721 (17 self)
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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
Discriminative Training and Maximum Entropy Models for Statistical Machine Translation
, 2002
"... We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language senten ..."
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Cited by 496 (29 self)
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We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language
Chunking with Maximum Entropy Models
, 2000
"... this paper I discuss a first attempt to create a text chunker using a Maximum Entropy model. The first experiments, implementing classifiers that tag every word in a sentence with a phrasetag using very local lexical information, partof speech tags and phrase tags of surrounding words, give encoura ..."
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Cited by 33 (1 self)
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this paper I discuss a first attempt to create a text chunker using a Maximum Entropy model. The first experiments, implementing classifiers that tag every word in a sentence with a phrasetag using very local lexical information, partof speech tags and phrase tags of surrounding words, give
A Maximum Entropy Model for PartOfSpeech Tagging
, 1996
"... This paper presents a statistical model which trains from a corpus annotated with PartOfSpeech tags and assigns them to previously unseen text with stateoftheart accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" t ..."
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Cited by 576 (1 self)
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This paper presents a statistical model which trains from a corpus annotated with PartOfSpeech tags and assigns them to previously unseen text with stateoftheart accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "
with Maximum Entropy Models
, 2006
"... Phonebased automatic language recognition systems have recently been surpassed in accuracy by systems that make use of spectral information alone. Intuitively, it seems that the higher level knowledge employed by a phonebased approach should enable it to achieve higher levels of accuracy than spec ..."
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present a discriminative approach to phonebased language recognition based on conditional maximum entropy models. We present results from our preliminary experiments with two types of backend classifiers on a tenlanguage task using short files of varying durations from the handsegmented and labeled
A Maximum Entropy Model For Parsing
 In Proceedings of the International Conference on Spoken Language Processing
"... this paper, we present a method where more of the tree structure is used in the parsing model. We define a set of features that capture long distance dependency such as parallelism in coordination. These features are then integrated with a Maximum Entropy model into an overall probabilistic model fo ..."
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Cited by 27 (2 self)
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this paper, we present a method where more of the tree structure is used in the parsing model. We define a set of features that capture long distance dependency such as parallelism in coordination. These features are then integrated with a Maximum Entropy model into an overall probabilistic model
Mixtures of Conditional Maximum Entropy Models
 In Proc. of ICML2003
, 2002
"... Driven by successes in several application areas, maximum entropy modeling has recently gained considerable popularity. We generalize the standard maximum entropy formulation of classi cation problems to better handle the case where complex data distributions arise from a mixture of simpler u ..."
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Cited by 14 (8 self)
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Driven by successes in several application areas, maximum entropy modeling has recently gained considerable popularity. We generalize the standard maximum entropy formulation of classi cation problems to better handle the case where complex data distributions arise from a mixture of simpler
A Gaussian prior for smoothing maximum entropy models
, 1999
"... In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood training for exponential models, and like other maximum likelihood methods is prone to overfitting of training data. Several smoothing methods for maximum entropy models have been proposed to address this problem ..."
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Cited by 251 (2 self)
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In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood training for exponential models, and like other maximum likelihood methods is prone to overfitting of training data. Several smoothing methods for maximum entropy models have been proposed to address
Feature Lattices for Maximum Entropy Modelling
 In Proc. of ACLCOLING
, 1998
"... Maximum entropy framework proved to be expressive and powerful for the statistical language modelling, but it suffers from the computational expensiveness of the model building. The iterative scaling algorithm that is used for the parameter estimation is computationally expensive while the feat ..."
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Cited by 37 (5 self)
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Maximum entropy framework proved to be expressive and powerful for the statistical language modelling, but it suffers from the computational expensiveness of the model building. The iterative scaling algorithm that is used for the parameter estimation is computationally expensive while
Maximum Entropy Models for Skin Detection
 In Proceedings Third Indian Conference on Computer Vision, Graphics and Image Processing
, 2002
"... We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from pract ..."
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Cited by 13 (2 self)
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We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from
Results 1  10
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2,289,413