Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks

Naohiro Ishii; Kazunori Iwata; Naoto Mukai; Kazuya Odagiri; Tokuro Matsuo

doi:10.1007/978-981-99-3236-8_13

Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks

Naohiro Ishii
, Kazunori Iwata
, Naoto Mukai
, Kazuya Odagiri
, Tokuro Matsuo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Recent developments of deep learning, machine learning, and artificial intelligence have a great influence on the wide areas of technologies. Classification is a core technology in their processing. This paper aims to make clear the classification performance for the bio-inspired asymmetric and symmetric networks. First, the bio-inspired asymmetric network is shown to have superior performance for tracing features compared to the symmetric one. Second, the classification characteristics of the asymmetric and symmetric networks are derived based on the independence of their outputs. Further, it is shown that generation of extended bases in the bio-inspired layered networks improves classification performance. Finally, the higher-dimensional mapping code generated as the extended bases are applied to the modified XOR problem.

Original language	English
Title of host publication	Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023
Editors	Xin-She Yang, R. Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	167-179
Number of pages	13
ISBN (Print)	9789819932351
DOIs	https://doi.org/10.1007/978-981-99-3236-8_13
Publication status	Published - 2024
Externally published	Yes
Event	8th International Congress on Information and Communication Technology, ICICT 2023 - London, United Kingdom Duration: 20-02-2023 → 23-02-2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	696 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	8th International Congress on Information and Communication Technology, ICICT 2023
Country/Territory	United Kingdom
City	London
Period	20-02-23 → 23-02-23

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-99-3236-8_13

Cite this

Ishii, N., Iwata, K., Mukai, N., Odagiri, K., & Matsuo, T. (2024). Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks. In X.-S. Yang, R. S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023 (pp. 167-179). (Lecture Notes in Networks and Systems; Vol. 696 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-3236-8_13

Ishii, Naohiro ; Iwata, Kazunori ; Mukai, Naoto et al. / Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks. Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023. editor / Xin-She Yang ; R. Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. pp. 167-179 (Lecture Notes in Networks and Systems).

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title = "Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks",

abstract = "Recent developments of deep learning, machine learning, and artificial intelligence have a great influence on the wide areas of technologies. Classification is a core technology in their processing. This paper aims to make clear the classification performance for the bio-inspired asymmetric and symmetric networks. First, the bio-inspired asymmetric network is shown to have superior performance for tracing features compared to the symmetric one. Second, the classification characteristics of the asymmetric and symmetric networks are derived based on the independence of their outputs. Further, it is shown that generation of extended bases in the bio-inspired layered networks improves classification performance. Finally, the higher-dimensional mapping code generated as the extended bases are applied to the modified XOR problem.",

keywords = "Asymmetric network, Classification performance, Independence for classification, Symmetric network, Tracing for features",

author = "Naohiro Ishii and Kazunori Iwata and Naoto Mukai and Kazuya Odagiri and Tokuro Matsuo",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 8th International Congress on Information and Communication Technology, ICICT 2023 ; Conference date: 20-02-2023 Through 23-02-2023",

year = "2024",

doi = "10.1007/978-981-99-3236-8\_13",

language = "English",

isbn = "9789819932351",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

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Ishii, N, Iwata, K, Mukai, N, Odagiri, K & Matsuo, T 2024, Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks. in X-S Yang, RS Sherratt, N Dey & A Joshi (eds), Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023. Lecture Notes in Networks and Systems, vol. 696 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 167-179, 8th International Congress on Information and Communication Technology, ICICT 2023, London, United Kingdom, 20-02-23. https://doi.org/10.1007/978-981-99-3236-8_13

Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks. / Ishii, Naohiro; Iwata, Kazunori; Mukai, Naoto et al.
Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023. ed. / Xin-She Yang; R. Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. p. 167-179 (Lecture Notes in Networks and Systems; Vol. 696 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Ishii, Naohiro

AU - Iwata, Kazunori

AU - Mukai, Naoto

AU - Odagiri, Kazuya

AU - Matsuo, Tokuro

PY - 2024

Y1 - 2024

N2 - Recent developments of deep learning, machine learning, and artificial intelligence have a great influence on the wide areas of technologies. Classification is a core technology in their processing. This paper aims to make clear the classification performance for the bio-inspired asymmetric and symmetric networks. First, the bio-inspired asymmetric network is shown to have superior performance for tracing features compared to the symmetric one. Second, the classification characteristics of the asymmetric and symmetric networks are derived based on the independence of their outputs. Further, it is shown that generation of extended bases in the bio-inspired layered networks improves classification performance. Finally, the higher-dimensional mapping code generated as the extended bases are applied to the modified XOR problem.

AB - Recent developments of deep learning, machine learning, and artificial intelligence have a great influence on the wide areas of technologies. Classification is a core technology in their processing. This paper aims to make clear the classification performance for the bio-inspired asymmetric and symmetric networks. First, the bio-inspired asymmetric network is shown to have superior performance for tracing features compared to the symmetric one. Second, the classification characteristics of the asymmetric and symmetric networks are derived based on the independence of their outputs. Further, it is shown that generation of extended bases in the bio-inspired layered networks improves classification performance. Finally, the higher-dimensional mapping code generated as the extended bases are applied to the modified XOR problem.

KW - Asymmetric network

KW - Classification performance

KW - Independence for classification

KW - Symmetric network

KW - Tracing for features

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Ishii N, Iwata K, Mukai N, Odagiri K, Matsuo T. Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks. In Yang XS, Sherratt RS, Dey N, Joshi A, editors, Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 167-179. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-3236-8_13

Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks

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