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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| Format: | Thesis |
| Language: | English |
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Curtin University
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/436 |