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ICML
2009
IEEE
16 years 7 months ago
Learning Markov logic network structure via hypergraph lifting
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. Learning ML...
Stanley Kok, Pedro Domingos
ICML
2007
IEEE
16 years 7 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
ISMIR
2005
Springer
164views Music» more  ISMIR 2005»
16 years 9 days ago
Theory and Evaluation of a Bayesian Music Structure Extractor
We introduce a new model for extracting classified structural segments, such as intro, verse, chorus, break and so forth, from recorded music. Our approach is to classify signal ...
Samer A. Abdallah, Katy Noland, Mark B. Sandler, M...
WWW
2010
ACM
15 years 7 months ago
LINKREC: a unified framework for link recommendation with user attributes and graph structure
With the phenomenal success of networking sites (e.g., Facebook, Twitter and LinkedIn), social networks have drawn substantial attention. On online social networking sites, link r...
Zhijun Yin, Manish Gupta, Tim Weninger, Jiawei Han
IJAR
2007
130views more  IJAR 2007»
15 years 6 months ago
Bayesian network learning algorithms using structural restrictions
The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered. These restrictions may codify expert knowledge in a given domai...
Luis M. de Campos, Javier Gomez Castellano