Please use this identifier to cite or link to this item:
|Title:||The use of hide in learning the value of a function|
|Keywords:||Convex optimization and noise data;Hypercircle inequality;Regularization;Reproducing kernel Hilbert space|
|Publisher:||10th WSEAS International Conference on Applied Computer Science, ACS'10|
|Abstract:||In this paper, we briefly review some recent work on Hypercircle inequality for data error (Hide) measured with square loss. We provide it in the case that the unit ball B is replaced by δB where δ is any positive number. We study the problem in learning the value of a function in reproducing kernel Hilbert space (RKHS) by using the available material from Hide with different values of δ. Moreover, we compare our numerical experiment to the method of regularization, which is the standard method for learning problem. We also discuss the effect of the values of δ on the learning task under consideration.|
|Appears in Collections:||Mathematics: International Proceedings|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.