A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Online Multiple Kernel Classification
Machine Learning
Multi class learning with individual sparsity
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We address the problem of learning classifiers using several kernel functions. On the contrary to many contributions in the field of learning from different sources of information using kernels, we here do not assume that the kernels used are positive definite. The learning problem that we are interested in involves a misclassification loss term and a regularization term that is expressed by means of a mixed norm. The use of a mixed norm allows us to enforce some sparsity structure, a particular case of which is, for instance, the Group Lasso. We solve the convex problem by employing proximal minimization algorithms, which can be viewed as refined versions of gradient descent procedures capable of naturally dealing with nondifferentiability. A numerical simulation on a Uci dataset shows the modularity of our approach.