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» Decision Making Using Probabilistic Inference Methods
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JMLR
2010
145views more  JMLR 2010»
14 years 9 months ago
Parallelizable Sampling of Markov Random Fields
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
James Martens, Ilya Sutskever
BMCBI
2010
110views more  BMCBI 2010»
15 years 2 months ago
TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach
Background: One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using mi...
Pietro Zoppoli, Sandro Morganella, Michele Ceccare...
296
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IBPRIA
2011
Springer
14 years 5 months ago
Classifying Melodies Using Tree Grammars
Abstract. Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show for pattern recognition in ge...
José Francisco Bernabeu, Jorge Calera-Rubio...
ICIP
2005
IEEE
16 years 4 months ago
Learning hidden semantic cues using support vector clustering
This paper presents a method to infer hidden semantic cues by accumulating the knowledge learned from relevance feedback sessions. We propose to explicitly represent a semantic sp...
Jia-Wen Tung, Chiou-Ting Hsu
ICMLA
2007
15 years 3 months ago
Soft Failure Detection Using Factorial Hidden Markov Models
In modern business, educational, and other settings, it is common to provide a digital network that interconnects hardware devices for shared access by the users (e.g., in an ofï¬...
Guillaume Bouchard, Jean-Marc Andreoli