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KDD
1995
ACM
148views Data Mining» more  KDD 1995»
15 years 6 months ago
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning
Knowledge discovery in databases has become an increasingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this p...
Philip K. Chan, Salvatore J. Stolfo
WWW
2004
ACM
16 years 3 months ago
Personalization in distributed e-learning environments
Personalized support for learners becomes even more important, when e-Learning takes place in open and dynamic learning and information networks. This paper shows how to realize p...
Peter Dolog, Nicola Henze, Wolfgang Nejdl, Michael...
UAI
2000
15 years 4 months ago
Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks
Algorithms for learning the conditional probabilities of Bayesian networks with hidden variables typically operate within a high-dimensional search space and yield only locally op...
Frank Wittig, Anthony Jameson
ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 3 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
ASPDAC
2007
ACM
108views Hardware» more  ASPDAC 2007»
15 years 7 months ago
Noise-Direct: A Technique for Power Supply Noise Aware Floorplanning Using Microarchitecture Profiling
This paper proposes Noise-Direct, a design methodology for power integrity aware floorplanning, using microarchitectural feedback to guide module placement. Stringent power constr...
Fayez Mohamood, Michael B. Healy, Sung Kyu Lim, Hs...