Minimum Modal Regression

Koichiro Yamauchi, Vanamala Narasimha Bhargav

Abstract

The recent development of microcomputers enables the execution of complex software in small embedded systems. Artificial intelligence is one form of software to be embedded into such devices. However, almost all embedded systems still have restricted storage space. One of the authors has already proposed an incremental learning method for regression, which works under a fixed storage space; however, this method cannot support the multivalued functions that usually appear in real-world problems. One way to support the multivalued function is to use the model regression method with a kernel density estimator. However, this method assumes that all sample points are recorded as kernel centroids, which is not suitable for small embedded systems. In this paper, we propose a minimum modal regression method that reduces the number of kernels using a projection method. The conditions required to maintain accuracy are derived through theoretical analysis. The experimental results show that our method reduces the number of kernels while maintaining a specified level of accuracy.

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Paper Citation


in Harvard Style

Yamauchi K. and Bhargav V. (2018). Minimum Modal Regression.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 448-455. DOI: 10.5220/0006601304480455


in Bibtex Style

@conference{icpram18,
author={Koichiro Yamauchi and Vanamala Narasimha Bhargav},
title={Minimum Modal Regression},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={448-455},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006601304480455},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Minimum Modal Regression
SN - 978-989-758-276-9
AU - Yamauchi K.
AU - Bhargav V.
PY - 2018
SP - 448
EP - 455
DO - 10.5220/0006601304480455