OcrV1, Main, Exploration, bibRecord, 001209

Modeling Context as Statistical Dependence

Identifieur interne : 001209 ( Main/Exploration ); précédent : 001208; suivant : 001210

Modeling Context as Statistical Dependence

Auteurs : Sriharsha Veeramachaneni [Italie] ; Prateek Sarkar [États-Unis] ; George Nagy (informaticien) [États-Unis]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.

RBID : ISTEX:50993A728920CA2485F7D3737DD68103573C8579

Abstract

Abstract: Theories of context in logic enable reasoning and deduction in contexts represented as formal objects. Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. With a series of examples based on frames of two patterns we show how each kind of dependence can be represented using graphical models and present examples from other disciplines where the particular dependence frequently occurs.

Url:

https://api.istex.fr/document/50993A728920CA2485F7D3737DD68103573C8579/fulltext/pdf

DOI: 10.1007/11508373_39

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">Abstract: Theories of context in logic enable reasoning and deduction in contexts represented as formal objects. Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. With a series of examples based on frames of two patterns we show how each kind of dependence can be represented using graphical models and present examples from other disciplines where the particular dependence frequently occurs.</div>
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Modeling Context as Statistical Dependence

Modeling Context as Statistical Dependence

Source :

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri