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    » Mixtures of Conditional Maximum Entropy Models
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    PAMI
    2008
    173views more  PAMI 2008»
    Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
    13 years 7 months ago
    Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
    Download  externe.emt.inrs.ca
    This study investigates Bayes classification of online Arabic characters using histograms of tangent differences and Gibbs modeling of the class-conditional probability density fun...
    Neila Mezghani, Amar Mitiche, Mohamed Cheriet
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    COLING
    2002
    110views Computational Linguistics» more  COLING 2002»
    A Maximum Entropy-based Word Sense Disambiguation System
    13 years 7 months ago
    A Maximum Entropy-based Word Sense Disambiguation System
    Download  www.dlsi.ua.es
    In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowl...
    Armando Suárez, Manuel Palomar
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    SDM
    2003
    SIAM
    110views Data Mining» more  SDM 2003»
    Mixture Models and Frequent Sets: Combining Global and Local Methods for 0-1 Data
    13 years 8 months ago
    Mixture Models and Frequent Sets: Combining Global and Local Methods for 0-1 Data
    Download  www.cs.ioc.ee
    We study the interaction between global and local techniques in data mining. Specifically, we study the collections of frequent sets in clusters produced by a probabilistic clust...
    Jaakko Hollmén, Jouni K. Seppänen, Hei...
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    SIAMAM
    2008
    119views more  SIAMAM 2008»
    The Chapman--Jouguet Closure for the Riemann Problem with Vaporization
    13 years 7 months ago
    The Chapman--Jouguet Closure for the Riemann Problem with Vaporization
    Download  hal.archives-ouvertes.fr
    This work is devoted to the modelling of phase transition. The thermodynamic model for phase transition chosen is a model with two equations of state, each of them modelling one p...
    Vincent Perrier
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    ICML
    2001
    IEEE
    266views Machine Learning» more  ICML 2001»
    Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
    14 years 8 months ago
    Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
    Download  www.cis.upenn.edu
    We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
    John D. Lafferty, Andrew McCallum, Fernando C. N. ...
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