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ICASSP
2009
IEEE
14 years 3 months ago
Map approach to learning sparse Gaussian Markov networks
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
Narges Bani Asadi, Irina Rish, Katya Scheinberg, D...
CIKM
2010
Springer
13 years 6 months ago
Regularization and feature selection for networked features
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
Hongliang Fei, Brian Quanz, Jun Huan
VLSISP
2002
112views more  VLSISP 2002»
13 years 8 months ago
Minimizing Buffer Requirements under Rate-Optimal Schedule in Regular Dataflow Networks
Large-grain synchronous dataflow graphs or multi-rate graphs have the distinct feature that the nodes of the dataflow graph fire at different rates. Such multi-rate large-grain dat...
Ramaswamy Govindarajan, Guang R. Gao, Palash Desai
KDD
2006
ACM
149views Data Mining» more  KDD 2006»
14 years 9 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
DAGM
2005
Springer
14 years 2 months ago
Regularization on Discrete Spaces
Abstract. We consider the classification problem on a finite set of objects. Some of them are labeled, and the task is to predict the labels of the remaining unlabeled ones. Such...
Dengyong Zhou, Bernhard Schölkopf