TY - GEN
T1 - Classification Performance in the Bio-inspired Asymmetric and Symmetric Networks
AU - Ishii, Naohiro
AU - Iwata, Kazunori
AU - Mukai, Naoto
AU - Odagiri, Kazuya
AU - Matsuo, Tokuro
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
UR - https://www.scopus.com/pages/publications/85174723632
UR - https://www.scopus.com/pages/publications/85174723632#tab=citedBy
U2 - 10.1007/978-981-99-3236-8_13
DO - 10.1007/978-981-99-3236-8_13
M3 - Conference contribution
AN - SCOPUS:85174723632
SN - 9789819932351
T3 - Lecture Notes in Networks and Systems
SP - 167
EP - 179
BT - Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023
A2 - Yang, Xin-She
A2 - Sherratt, R. Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Congress on Information and Communication Technology, ICICT 2023
Y2 - 20 February 2023 through 23 February 2023
ER -