Using Metalearning for Parameter Tuning in Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Catarina Félix (Author) Other; Carlos Soares (Author) FEUP; Alípio Jorge (Author) FCUP; Hugo Ferreira (Author) Other

Conference proceedings International

Title: European Congress on Computational Methods in Applied Sciences and Engineering: ECCOMAS 2017: VipIMAGE 2017

Pages: 1081-1090

6th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE)

Porto, PORTUGAL, OCT 18-20, 2017

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Other information

Authenticus ID: P-00N-53M

DOI: 10.1007/978-3-319-68195-5_120

Abstract (EN): Neural networks have been applied as a machine learning tool in many different areas. Recently, they have gained increased attention with what is now called deep learning. Neural networks algorithms have several parameters that need to be tuned in order to maximize performance. The definition of these parameters can be a difficult, extensive and time consuming task, even for expert users. One approach that has been successfully used for algorithm and parameter selection is metalearning. Metalearning consists in using machine learning algorithm on (meta)data from machine learning experiments to map the characteristics of the data with the performance of the algorithms. In this paper we study how a metalearning approach can be used to obtain a good set of parameters to learn a neural network for a given new dataset. Our results indicate that with metalearning we can successfully learn classifiers from past learning tasks that are able to define appropriate parameters.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10