Advances in learning with kernels: Theory and practice in a world of growing constraints

Kernel methods consistently outperformed previous generations of learning techniques. They provide a flexible and expressive learning framework that has been successfully applied to a wide range of real world problems but, recently, novel algorithms, such as Deep Neural Networks and Ensemble Methods, have increased their competitiveness against them. Due to the current data growth in size, heterogeneity and structure, the new generation of algorithms are expected to solve increasingly challenging problems. This must be done under growing constraints such as computational resources, memory budget and energy consumption. For these reasons, new ideas have to come up in the field of kernel learning, such as deeper kernels and novel algorithms, to fill the gap that now exists with the most recent learning paradigms. The purpose of this special session is to highlight recent advances in learning with kernels. In particular, this session welcomes contributions toward the solution of the weaknesses (e.g. scalability, computational efficiency and too shallow kernels) and the improvement of the strengths (e.g. the ability of dealing with structural data) of the state of the art kernel methods. We also encourage the submission of new theoretical results in the Statistical Learning Theory framework and innovative solutions to real world problems.