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MaximumEntropy Parameter Estimation for the . . .
 IN: PROCEEDINGS OF THE 16TH BELGIANDUTCH CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2004
"... We introduce an extension of the modified valuedifference kernel of knn by replacing the kernel’s default class distribution matrix with the matrix produced by the maximumentropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybrid ..."
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Cited by 3 (0 self)
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We introduce an extension of the modified valuedifference kernel of knn by replacing the kernel’s default class distribution matrix with the matrix produced by the maximumentropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybrid
A Comparison of Algorithms for Maximum Entropy Parameter Estimation
"... A comparison of algorithms for maximum entropy parameter estimation Conditional maximum entropy (ME) models provide a general purpose machine learning technique which has been successfully applied to fields as diverse as computer vision and econometrics, and which is used for a wide variety of class ..."
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Cited by 285 (2 self)
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A comparison of algorithms for maximum entropy parameter estimation Conditional maximum entropy (ME) models provide a general purpose machine learning technique which has been successfully applied to fields as diverse as computer vision and econometrics, and which is used for a wide variety
MaximumEntropy Parameter Estimation for the knn Modified ValueDifference Kernel
"... Abstract We introduce an extension of the modified valuedifference kernel of knn by replacing the kernel's default class distribution matrix with the matrix produced by the maximumentropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, compari ..."
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Abstract We introduce an extension of the modified valuedifference kernel of knn by replacing the kernel's default class distribution matrix with the matrix produced by the maximumentropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks
A Maximum Entropy approach to Natural Language Processing
 COMPUTATIONAL LINGUISTICS
, 1996
"... The concept of maximum entropy can be traced back along multiple threads to Biblical times. Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. In this paper we des ..."
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Cited by 1341 (5 self)
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The concept of maximum entropy can be traced back along multiple threads to Biblical times. Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. In this paper we
Maximum entropy markov models for information extraction and segmentation
, 2000
"... Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial ..."
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Cited by 554 (18 self)
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, capitalization, formatting, partofspeech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential models that represent the probability of a state given an observation and the previous state. We
A MaximumEntropyInspired Parser
, 1999
"... We present a new parser for parsing down to Penn treebank style parse trees that achieves 90.1% average precision/recall for sentences of length 40 and less, and 89.5% for sentences of length 100 and less when trained and tested on the previously established [5,9,10,15,17] "stan dard" se ..."
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Cited by 963 (19 self)
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" sections of the Wall Street Journal tree bank. This represents a 13% decrease in error rate over the best singleparser results on this corpus [9]. The major technical innova tion is the use of a "maximumentropyinspired" model for conditioning and smoothing that let us successfully to test
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 577 (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 "
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 497 (30 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
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2109 (30 self)
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of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximumlikelihood programs and much higher than the performance
A maximum likelihood approach to continuous speech recognition
 IEEE Trans. Pattern Anal. Machine Intell
, 1983
"... AbstractSpeech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the ..."
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Cited by 472 (9 self)
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with them. Index TermsMarkov models, maximum likelihood, parameter estimation, speech recognition, statistical models. I.
Results 1  10
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