Joint maximum a posteriori estimation of transformation and hidden Markov model parameters

2002 ◽  
Author(s):  
O. Siohan ◽  
C. Chesta ◽  
Chin-Hui Lee
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Maximum A Posteriori ◽  
Model Parameters ◽  
A Posteriori ◽  
Maximum A Posteriori Estimation ◽  
Posteriori Estimation

scholarly journals Towards Describing Visual Explanation using Machine Learning

2020 ◽  
Vol 9 (2) ◽  
pp. 1699-1703
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Category Label ◽  
Model Parameters ◽  
A Posteriori ◽  
Class Prediction ◽  
Systems Research ◽  
Special Places

Current available visible explanation generating systems research to easily absolve a class prediction. Still, they may additionally point out visible parameters attribute which replicate a strong category prior, though the proof may additionally not clearly be in the pic. This is specifically regarding as alternatively such marketers fail in constructing have confidence with human users. We proposed our own version, which makes a speciality of the special places of house of the seen item, together predicts the category label & interprets why the expected label is proper for the image. The machine proposes to annotate the images automatically using the Markov cache model. To annotate images, principles are represented as states through the usage of Hidden Markov model. The model parameters were estimated as part of a set of images and manual annotations. This is a great collection of checks, albeit automatically, with the possibility a posteriori of the concepts presented in her.

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