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arXiv · 1811.02361

Kalman Filter Modifier for Neural Networks in Non-stationary Environments

Honglin Li·Frieder Ganz·Shirin Enshaeifar·Payam Barnaghi
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Abstract

Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment. Learning new tasks without forgetting the previous knowledge is a challenge issue in machine learning. We propose a Kalman Filter based modifier to maintain the performance of Neural Network models under non-stationary environments. The result shows that our proposed model can preserve the key information and adapts better to the changes. The accuracy of proposed model decreases by 0.4% in our experiments, while the accuracy of conventional model decreases by 90% in the drifts environment.

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Honglin Li, Frieder Ganz, Shirin Enshaeifar, Payam Barnaghi. 2018-11-06. Kalman Filter Modifier for Neural Networks in Non-stationary Environments. https://arxiv.org/abs/1811.02361

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