Kernel Function Tuning for Single-Layer Neural Networks

Authors: Petra Vidnerová, Roman Neruda
Publication year: 2018
Publication type: Článek v odborném periodiku
Periodical: International Journal of Machine Learning and Computing
Projects: Model complexity of neural, radial, and kernel networks
Bibliographic citation: VIDNEROVÁ, Petra, NERUDA, Roman. Kernel Function Tuning for Single-Layer Neural Networks. International Journal of Machine Learning and Computing. 2018, 8(4), 354-360. ISSN 2010-3700. Dostupné z: http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=79&id=831
ID: 493061
Online record: View online record

Abstract

This paper describes an unified learning framework for kernel networks with one hidden layer, including models like radial basis function networks and regularization networks. The learning procedure consists of meta-parameter tuning wrapping the standard parameter optimization part. Several variants of learning are described and tested on various classification and regression problems. It is shown that meta-learning can improve the performance of models for the price of higher time complexity. © 2018, International Association of Computer Science and Information Technology.