Experimental study on parameter choices in norm-r support vector regression machines with noisy input

摘要

In [1], with the evidence framework, the almost inversely linear dependency between the optimal parameter r in norm-r support vector regression machine r-SVR and the Gaussian input noise is theoretically derived. When r takes a non-integer value, r-SVR cannot be easily realized using the classical QP optimization method. This correspondence attempts to achieve two goals: (1) The Newton-decent-method based implementation procedure of r-SVR is presented here; (2) With this procedure, the experimental studies on the dependency between the optimal parameter r in r-SVR and the Gaussian noisy input are given. Our experimental results here confirm the theoretical claim in [1].