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» Sparseness Achievement in Hidden Markov Models
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ICIP
2001
IEEE
14 years 7 months ago
A comparison of discrete and continuous output modeling techniques for a pseudo-2D hidden Markov model face recognition system
Face recognition has become an important topic within the field of pattern recognition and computer vision. In this field a number of different approaches to feature extraction, m...
Frank Wallhoff, Stefan Eickeler, Gerhard Rigoll
ICML
2009
IEEE
14 years 7 months ago
Exploiting sparse Markov and covariance structure in multiresolution models
We consider Gaussian multiresolution (MR) models in which coarser, hidden variables serve to capture statistical dependencies among the finest scale variables. Tree-structured MR ...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
NAACL
1994
13 years 7 months ago
Japanese Word Segmentation by Hidden Markov Model
The processing of Japanese text is complicated by the fact that there are no word delimiters. To segment Japanese text, systems typically use knowledge-based methods and large lex...
Constantine Papageorgiou
CORR
2010
Springer
91views Education» more  CORR 2010»
13 years 1 months ago
Switching between Hidden Markov Models using Fixed Share
In prediction with expert advice the goal is to design online prediction algorithms that achieve small regret (additional loss on the whole data) compared to a reference scheme. I...
Wouter M. Koolen, Tim van Erven
ICASSP
2011
IEEE
12 years 10 months ago
A non-negative approach to semi-supervised separation of speech from noise with the use of temporal dynamics
We present a semi-supervised source separation methodology to denoise speech by modeling speech as one source and noise as the other source. We model speech using the recently pro...
Gautham J. Mysore, Paris Smaragdis