Machine Learning with Dual Process Models

Bert Klauninger, Martin Unger, Horst Eidenberger

Abstract

Similarity measurement processes are a core part of most machine learning algorithms. Traditional approaches focus on either taxonomic or thematic thinking. Psychological research suggests that a combination of both is needed to model human-like similarity perception adequately. Such a combination is called a Similarity Dual Process Model (DPM). This paper describes how to construct DPMs as a linear combination of existing measures of similarity and distance. We use generalisation functions to convert distance into similarity. DPMs are similar to kernel functions. Thus, they can be integrated into any machine learning algorithm that uses kernel functions.Clearly, not all DPMs that can be formulated work equally well. Therefore we test classification performance in a real-world task: the detection of pedestrians in images. We assume that DPMs are only viable if they yield better classifiers than their constituting parts. In our experiments, we found DPM kernels that matched the performance of conventional ones for our data set. Eventually, we provide a construction kit to build such kernels to encourage further experiments in other application domains of machine learning.

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Paper Citation


in Harvard Style

Klauninger B., Unger M. and Eidenberger H. (2016). Machine Learning with Dual Process Models . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 148-153. DOI: 10.5220/0005655901480153


in Bibtex Style

@conference{icpram16,
author={Bert Klauninger and Martin Unger and Horst Eidenberger},
title={Machine Learning with Dual Process Models},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={148-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005655901480153},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Machine Learning with Dual Process Models
SN - 978-989-758-173-1
AU - Klauninger B.
AU - Unger M.
AU - Eidenberger H.
PY - 2016
SP - 148
EP - 153
DO - 10.5220/0005655901480153