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JMLR
2012
11 years 7 months ago
Algorithms for Learning Kernels Based on Centered Alignment
This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uni...
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
IJON
2006
87views more  IJON 2006»
13 years 4 months ago
Translation-invariant classification of non-stationary signals
Non-stationary signal classification is a complex problem. This problem becomes even more difficult if we add the following hypothesis: each signal includes a discriminant wavefor...
Vincent Guigue, Alain Rakotomamonjy, Stépha...
ESANN
2007
13 years 6 months ago
Markovian blind separation of non-stationary temporally correlated sources
In a previous work, we developed a quasi-efficient maximum likelihood approach for blindly separating stationary, temporally correlated sources modeled by Markov processes. In this...
Rima Guidara, Shahram Hosseini, Yannick Deville
COLT
2005
Springer
13 years 10 months ago
Learning Convex Combinations of Continuously Parameterized Basic Kernels
We study the problem of learning a kernel which minimizes a regularization error functional such as that used in regularization networks or support vector machines. We consider thi...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
AAAI
2012
11 years 7 months ago
Learning the Kernel Matrix with Low-Rank Multiplicative Shaping
Selecting the optimal kernel is an important and difficult challenge in applying kernel methods to pattern recognition. To address this challenge, multiple kernel learning (MKL) ...
Tomer Levinboim, Fei Sha