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ICML
2005
IEEE
14 years 7 months ago
Learning first-order probabilistic models with combining rules
Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models de...
Sriraam Natarajan, Prasad Tadepalli, Eric Altendor...
BMCBI
2006
119views more  BMCBI 2006»
13 years 6 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt
WWW
2005
ACM
14 years 7 months ago
Hybrid semantic tagging for information extraction
The semantic web is expected to have an impact at least as big as that of the existing HTML based web, if not greater. However, the challenge lays in creating this semantic web an...
Ronen Feldman, Binyamin Rosenfeld, Moshe Fresko, B...
IJIT
2004
13 years 8 months ago
Modeling of Pulping of Sugar Maple Using Advanced Neural Network Learning
This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear un...
W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sa...
ECCV
2002
Springer
14 years 8 months ago
Audio-Video Sensor Fusion with Probabilistic Graphical Models
Abstract. We present a new approach to modeling and processing multimedia data. This approach is based on graphical models that combine audio and video variables. We demonstrate it...
Matthew J. Beal, Hagai Attias, Nebojsa Jojic