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» Approximation Algorithms for Clustering Problems
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ALMOB
2006
80views more  ALMOB 2006»
15 years 7 months ago
Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics
The technique of Finite Markov Chain Imbedding (FMCI) is a classical approach to complex combinatorial problems related to sequences. In order to get efficient algorithms, it is k...
Grégory Nuel
267
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BMCBI
2006
202views more  BMCBI 2006»
15 years 7 months ago
Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks
Background: The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the sea...
David J. Reiss, Nitin S. Baliga, Richard Bonneau
213
Voted
BMCBI
2006
153views more  BMCBI 2006»
15 years 7 months ago
Automatic document classification of biological literature
Background: Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, ...
David Chen, Hans-Michael Müller, Paul W. Ster...
169
Voted
JMLR
2010
105views more  JMLR 2010»
15 years 1 months ago
Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials
Many structured information extraction tasks employ collective graphical models that capture interinstance associativity by coupling them with various clique potentials. We propos...
Rahul Gupta, Sunita Sarawagi, Ajit A. Diwan
NIPS
1998
15 years 8 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore