On conditional random fields: applications, feature selection, parameter estimation and hierarchical modelling

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On conditional random fields: applications, feature selection, parameter estimation and hierarchical modelling

There has been a growing interest in stochastic modelling and learning with complex data, whose elements are structured and interdependent. One of the most successful methods to model data dependencies is graphical models, which is a combination of graph theory and probability theory. This thesis fo...

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Bibliographic Details
Main Author:	Tran, The Truyen
Format:	Thesis
Language:	English
Published:	Curtin University 2008
Subjects:	Vietnamese accent restoration graph and probability theory graphical models Conditional Random Fields (CRF) complex data Markov network stochastic modelling
Online Access:	http://hdl.handle.net/20.500.11937/436

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