A Discriminative Nonparametric Bayesian Model: Infinite Hidden Conditional Random Fields (bibtex)
by Bousmalis, Konstantinos, Morency, Louis-Philippe, Zafeiriou, Stefanos and Pantic, Maja
Abstract:
Nonparametric methods have been successfully applied to many existing graphical models with latent variables [3, 2, 7, 4]. In contrast to all previous work, the infinite Hidden Conditional Random Fields (iHCRF), introduced in this work, is the first, to our knowledge, discriminative bayesian nonparametric model.
Reference:
A Discriminative Nonparametric Bayesian Model: Infinite Hidden Conditional Random Fields (Bousmalis, Konstantinos, Morency, Louis-Philippe, Zafeiriou, Stefanos and Pantic, Maja), In Neural Information Processing Systems (NIPS) Workshop on Bayesian Nonparametrics, 2011.
Bibtex Entry:
@inproceedings{bousmalis_discriminative_2011,
	address = {Sierra Nevada, Spain},
	title = {A {Discriminative} {Nonparametric} {Bayesian} {Model}: {Infinite} {Hidden} {Conditional} {Random} {Fields}},
	url = {http://ict.usc.edu/pubs/A%20Discriminative%20Nonparametric%20Bayesian%20Model-%20In%EF%AC%81nite%20Hidden%20Conditional%20Random%20Fields.pdf},
	abstract = {Nonparametric methods have been successfully applied to many existing graphical models with latent variables [3, 2, 7, 4]. In contrast to all previous work, the infinite Hidden Conditional Random Fields (iHCRF), introduced in this work, is the first, to our knowledge, discriminative bayesian nonparametric model.},
	booktitle = {Neural {Information} {Processing} {Systems} ({NIPS}) {Workshop} on {Bayesian} {Nonparametrics}},
	author = {Bousmalis, Konstantinos and Morency, Louis-Philippe and Zafeiriou, Stefanos and Pantic, Maja},
	month = dec,
	year = {2011},
	keywords = {Virtual Humans}
}
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