Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

11

ICIAP
2007
ACM

favoriteEmaildiscussreport

127views Image Processing» more ICIAP 2007»

Sparseness Achievement in Hidden Markov Models

14 years 4 months ago

Sparseness Achievement in Hidden Markov Models

Download eprints.uniss.it

In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irrelevant parameters are set exactly to zero. Alternatively to standard Maximum Likelihood Estimation (Baum Welch training), in the proposed approach the parameters estimation problem is cast into a Bayesian framework, with

Manuele Bicego, Marco Cristani, Vittorio Murino

Real-time Traffic

Baum Welch Training | ICIAP 2007 | Image Processing | Maximum Likelihood Estimation | Parameters Estimation Problem |

claim paper

Related Content

» Waveletdomain compressive signal reconstruction using a Hidden Markov Tree model

» Embedded Profile Hidden Markov Models for Shape Analysis

» A Profile Hidden Markov Model Framework for Modeling and Analysis of Shape

» Shape Analysis Using CurvatureBased Descriptors and Profile Hidden Markov Models

» Word recognition and incremental learning based on neural associative memories and hidden ...

» Gaussian multiresolution models exploiting sparse Markov and covariance structure

» A Fast Fertility Hidden Markov Model for Word Alignment Using MCMC

» Monte Carlo Hidden Markov Models Learning NonParametric Models of Partially Observable Sto...

» Robust highlight extraction using multistream hidden Markov models for baseball video

Post Info
More Details (n/a)

Added	08 Dec 2009
Updated	08 Dec 2009
Type	Conference
Year	2007
Where	ICIAP
Authors	Manuele Bicego, Marco Cristani, Vittorio Murino

Comments (0)