A Multi Class Classification to Detect Original Form of Kaomoji using Neural Network

Noriyuki Okumura; Rei Okumura

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Paper

A Multi Class Classification to Detect Original Form of Kaomoji using Neural Network

Topics: Communication, Collaboration and Information Sharing; Enterprise Information Systems; Intelligent Information Systems

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 0IC3K, 377-382, 2019 , Vienna, Austria

Authors: Noriyuki Okumura ¹ and Rei Okumura ²

Affiliations: ¹ Faculty of Modern Social Studies, Otemae University and Japan ; ² Advanced Course of Mechanical and Electronic System Engineering, National Institute of Technology, Akashi College and Japan

Keyword(s): Kaomoji, Original Form, Neural Network, Middle Layer.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Communication, Collaboration and Information Sharing ; Enterprise Information Systems ; Intelligent Information Systems ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Symbolic Systems

Abstract: In this paper, we propose a multi-class classification method for Kaomoji using feed forward neural network. Neural network has some units in each layer, but the suitable number of units is not clear. This research investigated the relation between the number of units and the accuracy of multi-class classification method.

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Paper citation in several formats:

Okumura, N. and Okumura, R. (2019). A Multi Class Classification to Detect Original Form of Kaomoji using Neural Network. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KMIS; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 377-382. DOI: 10.5220/0008366203770382

@conference{kmis19,
author={Noriyuki Okumura. and Rei Okumura.},
title={A Multi Class Classification to Detect Original Form of Kaomoji using Neural Network},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KMIS},
year={2019},
pages={377-382},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008366203770382},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KMIS
TI - A Multi Class Classification to Detect Original Form of Kaomoji using Neural Network
SN - 978-989-758-382-7
IS - 2184-3228
AU - Okumura, N.
AU - Okumura, R.
PY - 2019
SP - 377
EP - 382
DO - 10.5220/0008366203770382
PB - SciTePress