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» Structural Inference of Hierarchies in Networks
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JMLR
2010
140views more  JMLR 2010»
15 years 29 days ago
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact r...
Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
ECAI
2004
Springer
15 years 11 months ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana
NIPS
1996
15 years 7 months ago
Continuous Sigmoidal Belief Networks Trained using Slice Sampling
Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean G...
Brendan J. Frey
BMCBI
2004
174views more  BMCBI 2004»
15 years 6 months ago
Implications for domain fusion protein-protein interactions based on structural information
Background: Several in silico methods exist that were developed to predict protein interactions from the copious amount of genomic and proteomic data. One of these methods is Doma...
Jer-Ming Chia, Prasanna R. Kolatkar
SBRN
2000
IEEE
15 years 10 months ago
An Evolutionary Immune Network for Data Clustering
This paper explores basic aspects of the immune system and proposes a novel immune network model with the main goals of clustering and filtering unlabeled numerical data sets. It ...
Leandro Nunes de Castro, Fernando J. Von Zuben