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SDM
2004
SIAM
142views Data Mining» more  SDM 2004»
14 years 11 months ago
Learning to Read Between the Lines: The Aspect Bernoulli Model
We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and ...
Ata Kabán, Ella Bingham, T. Hirsimäki
ICDM
2010
IEEE
127views Data Mining» more  ICDM 2010»
14 years 8 months ago
Learning Markov Network Structure with Decision Trees
Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
Daniel Lowd, Jesse Davis
JAIR
2007
127views more  JAIR 2007»
14 years 10 months ago
Learning Symbolic Models of Stochastic Domains
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a a new probabilistic planning rule representation to compactly ...
Hanna M. Pasula, Luke S. Zettlemoyer, Leslie Pack ...
WIOPT
2010
IEEE
14 years 8 months ago
Stochastic modeling of carrier sensing based cognitive radio networks
In this paper, we propose a comprehensive probabilistic framework which can be used to model and analyze cognitive radio (CR) network using carrier sensing (CS) based multiple acc...
Nguyen Tien Viet, François Baccelli
ICASSP
2011
IEEE
14 years 1 months ago
Efficient implementation of probabilistic multi-pitch tracking
We significantly improve the computational efficiency of a probabilistic approach for multiple pitch tracking. This method is based on a factorial hidden Markov model and two al...
Michael Wohlmayr, Robert Peharz, Franz Pernkopf