Mixture of Multilayer Perceptron Regressions

Ryohei Nakano, Seiya Satoh

2019

Abstract

This paper investigates mixture of multilayer perceptron (MLP) regressions. Although mixture of MLP regressions (MoMR) can be a strong fitting model for noisy data, the research on it has been rare. We employ soft mixture approach and use the Expectation-Maximization (EM) algorithm as a basic learning method. Our learning method goes in a double-looped manner; the outer loop is controlled by the EM and the inner loop by MLP learning method. Given data, we will have many models; thus, we need a criterion to select the best. Bayesian Information Criterion (BIC) is used here because it works nicely for MLP model selection. Our experiments showed that the proposed MoMR method found the expected MoMR model as the best for artificial data and selected the MoMR model having smaller error than any linear models for real noisy data.

Paper Citation

in Harvard Style

Nakano R. and Satoh S. (2019). Mixture of Multilayer Perceptron Regressions.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 509-516. DOI: 10.5220/0007367405090516

in Bibtex Style

@conference{icpram19,
author={Ryohei Nakano and Seiya Satoh},
title={Mixture of Multilayer Perceptron Regressions},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={509-516},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007367405090516},
isbn={978-989-758-351-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Mixture of Multilayer Perceptron Regressions
SN - 978-989-758-351-3
AU - Nakano R.
AU - Satoh S.
PY - 2019
SP - 509
EP - 516
DO - 10.5220/0007367405090516