Kernel Function Tuning for Single-Layer Neural Networks

0493061 - ÚI 2019 RIV SG eng J - Článek v odborném periodiku
Vidnerová, Petra - Neruda, Roman
Kernel Function Tuning for Single-Layer Neural Networks.
International Journal of Machine Learning and Computing. Roč. 8, č. 4 (2018), s. 354-360. ISSN 2010-3700
Grant CEP: GA ČR GA15-18108S
Institucionální podpora: RVO:67985807
Klíčová slova: radial basis function networks * shallow neural networks * kernel methods * hyper-parameter tuning
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=79&id=831

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.
Trvalý link: http://hdl.handle.net/11104/0286524