8 On Kernel Target Alignment

@inproceedings{Cristianini8OK,
  title={8 On Kernel Target Alignment},
  author={N. Cristianini and Jaz S. Kandola and Andr{\'e} Elisseeff and John Shawe-Taylor}
}
Kernel based methods are increasingly being used for data modelling because of their conceptual simplicity and outstanding performance on many tasks. However, in practice the kernel function is often chosen using trial-and-error heuristics. In this paper we address the problem of measuring the degree of agreement between a kernel and a learning task. We propose a quantity to capture this notion, which we call alignment. We study its theoretical properties, and derive a series of simple… CONTINUE READING