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ICML
2005
IEEE
14 years 6 months ago
Learning the structure of Markov logic networks
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
Stanley Kok, Pedro Domingos
BMCBI
2005
190views more  BMCBI 2005»
13 years 5 months ago
Species-specific analysis of protein sequence motifs using mutual information
Background: Protein sequence motifs are by definition short fragments of conserved amino acids, often associated with a specific function. Accordingly protein sequence profiles de...
Jan Hummel, Nima Keshvari, Wolfram Weckwerth, Joac...
HRI
2007
ACM
13 years 9 months ago
Efficient model learning for dialog management
Intelligent planning algorithms such as the Partially Observable Markov Decision Process (POMDP) have succeeded in dialog management applications [10, 11, 12] because of their rob...
Finale Doshi, Nicholas Roy
FLAIRS
2009
13 years 3 months ago
Analyzing Team Actions with Cascading HMM
While team action recognition has a relatively extended literature, less attention has been given to the detailed realtime analysis of the internal structure of the team actions. ...
Brandyn Allen White, Nate Blaylock, Ladislau B&oum...
AAAI
2004
13 years 7 months ago
Additive versus Multiplicative Clause Weighting for SAT
This paper examines the relative performance of additive and multiplicative clause weighting schemes for propositional satisfiability testing. Starting with one of the most recent...
John Thornton, Duc Nghia Pham, Stuart Bain, Valnir...