Hierarchical semi-markov conditional random fields for recursive sequential data
Contribuinte(s) |
Koller, Daphne Bengio, Yoshua Schuurmans, Dale Bottou, Leon Culotta, Aron |
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Data(s) |
01/01/2008
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Resumo |
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorithms for learning and constrained inference in a partially-supervised setting, which is important issue in practice where labels can only be obtained sparsely. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Curran Associates |
Relação |
http://dro.deakin.edu.au/eserv/DU:30044754/phung-hierarchicalsemi-2008.pdf http://dro.deakin.edu.au/eserv/DU:30044754/phung-hierarchicalsemi-evidence-2008.pdf |
Direitos |
2009, Curran Associates |
Palavras-Chave | #efficient algorithm #generalisation #hierarchical model #human activities #indoor surveillance #model complexes #observed data #polynomial-time algorithms #reasonable accuracy #semi-Markov #sequential data |
Tipo |
Conference Paper |