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Bayesian and L1 Approaches to Sparse Unsupervised Learning
Bayesian L1 Approaches Sparse Unsupervised Learning
2011/7/6
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborat...
Model Selection by Loss Rank for Classification and Unsupervised Learning
Classification graphical models loss rank principle model selection
2010/11/9
Hutter (2007) recently introduced the loss rank principle (LoRP) as a general-purpose principle for model selection. The LoRP enjoys many attractive prop-erties and deserves further investigations. Th...