A unified framework for Support Vector Machines, Multiple Kernel Learning and metric learning

(2011), we propose a more gen- eral KDE formulation (prediction and pre-image steps) based on operator-valued (multi-task) kernels instead of scalar-valued ones used by the

Using tools from functional analysis, and in particular covariance operators, we extend the consistency results to this infinite dimensional case and also propose an adaptive scheme

• Our seond KPCA based transfer learning approah (KPCA-TL-LT) further aligns the resulting mathed KPCA representations of soure and target by a linear transformation.. The result

Standard SVM optimization algorithms are available, but also more sophisticated learning-based kernel combination methods such as Multiple Kernel Learning (MKL), and a

We believe that although the problem of detection has to be solved by the use of deep learning based on convolutional neural networks, in the same way that current breakthroughs

Second, the model is obtained through a local optimization process (we propose an implementation for two algorithms: the Nelder-Mead algorithm [8] and the particle swarm

observations in this experiment but it is not used in the regression algorithm. This is a specific form of the Bellman equation [10] with the transformation t being a

• we propose a block-coordinate descent algorithm to solve the MovKL opti- mization problem which involves solving a challenging linear system with a sum of block operator matrices,