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NIPS
2008
13 years 8 months ago
Learning with Consistency between Inductive Functions and Kernels
Regularized Least Squares (RLS) algorithms have the ability to avoid over-fitting problems and to express solutions as kernel expansions. However, we observe that the current RLS ...
Haixuan Yang, Irwin King, Michael R. Lyu
ICML
2009
IEEE
14 years 8 months ago
Proximal regularization for online and batch learning
Many learning algorithms rely on the curvature (in particular, strong convexity) of regularized objective functions to provide good theoretical performance guarantees. In practice...
Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo
ADMA
2009
Springer
246views Data Mining» more  ADMA 2009»
14 years 2 months ago
Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach
Image spam is a new trend in the family of email spams. The new image spams employ a variety of image processing technologies to create random noises. In this paper, we propose a s...
Yan Gao, Ming Yang, Alok N. Choudhary
BMCBI
2006
101views more  BMCBI 2006»
13 years 7 months ago
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms
Background: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validatio...
Tim Van den Bulcke, Koen Van Leemput, Bart Naudts,...
ICIP
2008
IEEE
14 years 1 months ago
An iterative algorithm for linear inverse problems with compound regularizers
In several imaging inverse problems, it may be of interest to encourage the solution to have characteristics which are most naturally expressed by the combination of more than one...
José M. Bioucas-Dias, Mário A. T. Fi...