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2017 | OriginalPaper | Buchkapitel

A Soft-Computing Approach for Quantification of Personal Perceptions

verfasst von : Luis Diago, Julian Romero, Junichi Shinoda, Hiroe Abe, Ichiro Hagiwara

Erschienen in: Advances in Affective and Pleasurable Design

Verlag: Springer International Publishing

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Abstract

Soft-computing forms the basis of a considerable amount of machine learning techniques which deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. This paper describes an application developed to understand what means a picture (portrait) to be Iyashi. The neuro-fuzzy quantification allowed extracting a set of 35 rules that describe the meaning of the word Iyashi to hundreds of users. Facial expressions of the subjects and their brain signals during the evaluation of the images have been explored to validate the obtained rules. The developed system allows discovering the rules that describe the preferences of users while exploring the space of possible design parameters so that the system predictions match the preferences of users. Interactive genetic algorithms (IGAs) have been used for the implementation of a color recommendation system following customer’s preferences. The combination of color and geometric shapes is also explored.

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Metadaten
Titel
A Soft-Computing Approach for Quantification of Personal Perceptions
verfasst von
Luis Diago
Julian Romero
Junichi Shinoda
Hiroe Abe
Ichiro Hagiwara
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-41661-8_20

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