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» Hidden Markov Models with Multiple Observation Processes
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FLAIRS
2008
15 years 2 months ago
Learning Dynamic Naive Bayesian Classifiers
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...
Miriam Martínez, Luis Enrique Sucar
ICML
2004
IEEE
16 years 18 days ago
Learning low dimensional predictive representations
Predictive state representations (PSRs) have recently been proposed as an alternative to partially observable Markov decision processes (POMDPs) for representing the state of a dy...
Matthew Rosencrantz, Geoffrey J. Gordon, Sebastian...
ALMOB
2006
155views more  ALMOB 2006»
14 years 12 months ago
A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons
Background: An important challenge in eukaryotic gene prediction is accurate identification of alternatively spliced exons. Functional transcripts can go undetected in gene expres...
Jonathan E. Allen, Steven L. Salzberg
ICIP
2007
IEEE
16 years 1 months ago
Image Denoising with Nonparametric Hidden Markov Trees
We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coefficients ...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...
ICB
2007
Springer
183views Biometrics» more  ICB 2007»
15 years 1 months ago
Factorial Hidden Markov Models for Gait Recognition
Gait recognition is an effective approach for human identification at a distance. During the last decade, the theory of hidden Markov models (HMMs) has been used successfully in th...
Changhong Chen, Jimin Liang, Haihong Hu, Licheng J...