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137
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ICML
2004
IEEE
16 years 4 months ago
Learning associative Markov networks
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in fields as diverse as image processing, natural language analysis, and bio...
Benjamin Taskar, Vassil Chatalbashev, Daphne Kolle...
112
Voted
ICML
2003
IEEE
16 years 4 months ago
On Kernel Methods for Relational Learning
Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
Chad M. Cumby, Dan Roth
142
Voted
KDD
2008
ACM
115views Data Mining» more  KDD 2008»
16 years 4 months ago
SPIRAL: efficient and exact model identification for hidden Markov models
Hidden Markov models (HMMs) have received considerable attention in various communities (e.g, speech recognition, neurology and bioinformatic) since many applications that use HMM...
Yasuhiro Fujiwara, Yasushi Sakurai, Masashi Yamamu...
KDD
2007
ACM
154views Data Mining» more  KDD 2007»
16 years 4 months ago
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and m...
Sitaram Asur, Srinivasan Parthasarathy, Duygu Ucar
161
Voted
KDD
2006
ACM
149views Data Mining» more  KDD 2006»
16 years 4 months ago
Regularized discriminant analysis for high dimensional, low sample size data
Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining, machine learning, and bioinformatics. Friedman proposed a compromise between Linear ...
Jieping Ye, Tie Wang