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CDC
2008
IEEE
145views Control Systems» more  CDC 2008»
13 years 7 months ago
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
ICML
2005
IEEE
14 years 8 months ago
Augmenting naive Bayes for ranking
Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is m...
Harry Zhang, Liangxiao Jiang, Jiang Su
ICML
2003
IEEE
14 years 8 months ago
Weighted Low-Rank Approximations
We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving weighted low-rank approximat...
Nathan Srebro, Tommi Jaakkola
IBPRIA
2007
Springer
13 years 11 months ago
Efficiently Downdating, Composing and Splitting Singular Value Decompositions Preserving the Mean Information
Three methods for the efficient downdating, composition and splitting of low rank singular value decompositions are proposed. They are formulated in a closed form, considering the ...
Javier Melenchón, Elisa Martínez
TNN
2010
127views Management» more  TNN 2010»
13 years 2 months ago
RAMOBoost: ranked minority oversampling in boosting
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imba...
Sheng Chen, Haibo He, Edwardo A. Garcia