loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Ryohei Nakano 1 and Seiya Satoh 2

Affiliations: 1 Chubu University, 1200 Matsumoto-cho, Kasugai and 487-8501 Japan ; 2 Tokyo Denki University, Ishizaka, Hatoyama-machi, Hiki-gun and Saitama 350-0394 Japan

Keyword(s): Mixture Models, Regression, Multilayer Perceptrons, EM Algorithm, Model Selection.

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.192.47.250

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Nakano, R. and Satoh, S. (2019). Mixture of Multilayer Perceptron Regressions. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 509-516. DOI: 10.5220/0007367405090516

@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 - ICPRAM},
year={2019},
pages={509-516},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007367405090516},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Mixture of Multilayer Perceptron Regressions
SN - 978-989-758-351-3
IS - 2184-4313
AU - Nakano, R.
AU - Satoh, S.
PY - 2019
SP - 509
EP - 516
DO - 10.5220/0007367405090516
PB - SciTePress