A platform for massive agent-based simulation and its evaluation
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
The artificial neuron has come a long way in modeling the functional capabilities of various neuronal processes. The higher order neurons have shown improved computational power and generalization ability. However, these models are difficult to train because of a combinatorial explosion of higher order terms as the number of inputs to the neuron increases. This work presents an artificial neural network using a neuron architecture called generalized mean neuron (GMN) model. This neuron model consists of an aggregation function which is based on the generalized mean of the all the inputs applied to it. The proposed neuron model with same number of parameters as the McCulloch-Pitts model demonstrates better computational power. The performance of this model has been benchmarked on both classification and time series prediction problems. © 2006 Elsevier B.V. All rights reserved.
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
Jennifer D'souza, Nandana Mihindukulasooriya
KGC 2024
Kenneth L. Clarkson, Elad Hazan, et al.
Journal of the ACM
Hironori Takeuchi, Tetsuya Nasukawa, et al.
Transactions of the Japanese Society for Artificial Intelligence