Adaptive Kernel Based Machine Learning Methods.

摘要

Research results obtained from this project address the kernel selection problem in machine learning. Specifically, motivated from the need of updating the current operator-valued reproducing kernel in multi-task learning when underfitting or overfitting occurs, we studied the construction of a refinement kernel for a given operator-valued reproducing kernel such that the vector-valued reproducing kernel Hilbert space of the refinement kernel contains that of the given kernel as a subspace. We also developed a complete characterization of multi-task finite rank kernels in terms of the positivity of what we call its associated characteristic operator.