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» Hidden Markov Models with Multiple Observation Processes
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ECCV
1998
Springer
16 years 6 months ago
A Two-Stage Probabilistic Approach for Object Recognition
Assume that some objects are present in an image but can be seen only partially and are overlapping each other. To recognize the objects, we have to rstly separate the objects from...
Stan Z. Li, Joachim Hornegger
SIGIR
2005
ACM
15 years 9 months ago
Generic soft pattern models for definitional question answering
This paper explores probabilistic lexico-syntactic pattern matching, also known as soft pattern matching. While previous methods in soft pattern matching are ad hoc in computing t...
Hang Cui, Min-Yen Kan, Tat-Seng Chua
IEEEVAST
2010
14 years 11 months ago
A visual analytics approach to model learning
The process of learning models from raw data typically requires a substantial amount of user input during the model initialization phase. We present an assistive visualization sys...
Supriya Garg, I. V. Ramakrishnan, Klaus Mueller
AAAI
2007
15 years 6 months ago
Scaling Up: Solving POMDPs through Value Based Clustering
Partially Observable Markov Decision Processes (POMDPs) provide an appropriately rich model for agents operating under partial knowledge of the environment. Since finding an opti...
Yan Virin, Guy Shani, Solomon Eyal Shimony, Ronen ...
CORR
2008
Springer
189views Education» more  CORR 2008»
15 years 4 months ago
Algorithms for Dynamic Spectrum Access with Learning for Cognitive Radio
We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperati...
Jayakrishnan Unnikrishnan, Venugopal V. Veeravalli