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» The Use of Classifiers in Sequential Inference
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ALMOB
2006
102views more  ALMOB 2006»
13 years 7 months ago
Mining, compressing and classifying with extensible motifs
Background: Motif patterns of maximal saturation emerged originally in contexts of pattern discovery in biomolecular sequences and have recently proven a valuable notion also in t...
Alberto Apostolico, Matteo Comin, Laxmi Parida
BMCBI
2010
229views more  BMCBI 2010»
13 years 7 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
ICA
2012
Springer
12 years 3 months ago
New Online EM Algorithms for General Hidden Markov Models. Application to the SLAM Problem
In this contribution, new online EM algorithms are proposed to perform inference in general hidden Markov models. These algorithms update the parameter at some deterministic times ...
Sylvain Le Corff, Gersende Fort, Eric Moulines
ICDM
2007
IEEE
162views Data Mining» more  ICDM 2007»
13 years 11 months ago
Exploiting Network Structure for Active Inference in Collective Classification
Active inference seeks to maximize classification performance while minimizing the amount of data that must be labeled ex ante. This task is particularly relevant in the context o...
Matthew J. Rattigan, Marc Maier, David Jensen, Bin...
COLING
2008
13 years 9 months ago
Modeling Semantic Containment and Exclusion in Natural Language Inference
We propose an approach to natural language inference based on a model of natural logic, which identifies valid inferences by their lexical and syntactic features, without full sem...
Bill MacCartney, Christopher D. Manning