Regularized kernel methods

The starting point of our work is the connection between learning theory and the theory of regularization of ill-posed inverse problems. Our goals are:

• to study consistency and finite sample bounds of regularized learning algorithms
• to design and develop learning algorithms based on spectral filters
• to investigate the problems of vector valued regression