Abstract
This paper presents a case study for the overlapped multi-neural-network (OMNN). An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different hidden units. Input-output spaces are partitioned into several parts, each of which corresponds to one subnet of OMNN. Numerical simulations show that such an OMNN has superior performance in that it has better presentation ability than an ordinary neural network and better generalization ability than a non-overlapped multi-neural-network.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | IEEE |
| Pages | 120-125 |
| Number of pages | 6 |
| Volume | 1 |
| Publication status | Published - 2000 |
| Externally published | Yes |
| Event | International Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy Duration: 2000 Jul 24 → 2000 Jul 27 |
Other
| Other | International Joint Conference on Neural Networks (IJCNN'2000) |
|---|---|
| City | Como, Italy |
| Period | 00/7/24 → 00/7/27 |
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ASJC Scopus subject areas
- Software
Cite this
Overlapped multi-neural-network : A case study. / Furuzuki, Takayuki; Hirasawa, Kotaro.
Proceedings of the International Joint Conference on Neural Networks. Vol. 1 Piscataway, NJ, United States : IEEE, 2000. p. 120-125.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Overlapped multi-neural-network
T2 - A case study
AU - Furuzuki, Takayuki
AU - Hirasawa, Kotaro
PY - 2000
Y1 - 2000
N2 - This paper presents a case study for the overlapped multi-neural-network (OMNN). An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different hidden units. Input-output spaces are partitioned into several parts, each of which corresponds to one subnet of OMNN. Numerical simulations show that such an OMNN has superior performance in that it has better presentation ability than an ordinary neural network and better generalization ability than a non-overlapped multi-neural-network.
AB - This paper presents a case study for the overlapped multi-neural-network (OMNN). An overlapped multi-neural-network, structurally, is the same as an ordinary feedforward neural network, but it is considered as one consisting of several subnets. All subnets have the same input-output units, but some different hidden units. Input-output spaces are partitioned into several parts, each of which corresponds to one subnet of OMNN. Numerical simulations show that such an OMNN has superior performance in that it has better presentation ability than an ordinary neural network and better generalization ability than a non-overlapped multi-neural-network.
UR - http://www.scopus.com/inward/record.url?scp=0033717739&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0033717739&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0033717739
VL - 1
SP - 120
EP - 125
BT - Proceedings of the International Joint Conference on Neural Networks
PB - IEEE
CY - Piscataway, NJ, United States
ER -