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» Algorithms for Learning Regular Expressions
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ICML
2004
IEEE
16 years 2 months ago
Multiple kernel learning, conic duality, and the SMO algorithm
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. ...
Francis R. Bach, Gert R. G. Lanckriet, Michael I. ...
CORR
2010
Springer
148views Education» more  CORR 2010»
14 years 8 months ago
A Unifying View of Multiple Kernel Learning
Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different for...
Marius Kloft, Ulrich Rückert, Peter L. Bartle...
PAMI
2008
135views more  PAMI 2008»
15 years 1 months ago
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
In this paper, we develop a new effective multiple kernel learning algorithm. First, we map the input data into m different feature spaces by m empirical kernels, where each genera...
Zhe Wang, Songcan Chen, Tingkai Sun
JCB
2007
130views more  JCB 2007»
15 years 1 months ago
Bayesian Inference of MicroRNA Targets from Sequence and Expression Data
MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although mu...
Jim C. Huang, Quaid Morris, Brendan J. Frey
ICML
2009
IEEE
16 years 2 months ago
Group lasso with overlap and graph lasso
We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is ty...
Laurent Jacob, Guillaume Obozinski, Jean-Philippe ...