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Kernels for Vector-Valued Functions: a Review
(2011-06-30)
Kernel methods are among the most popular techniques in machine learning. From a frequentist/discriminative perspective they play a central role in regularization theory as they provide a natural choice for the hypotheses ...
A Note on Perturbation Results for Learning Empirical Operators
(2008-08-19)
A large number of learning algorithms, for example, spectral clustering, kernel Principal Components Analysis and many manifold methods are based on estimating eigenvalues and eigenfunctions of operators defined by a ...
Generalization and Properties of the Neural Response
(2010-11-19)
Hierarchical learning algorithms have enjoyed tremendous growth in recent years, with many new algorithms being proposed and applied to a wide range of applications. However, despite the apparent success of hierarchical ...