Unlearned Class Estimation Neural Network Based on Approximate GMM for FPGA Implementation

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  • FPGA実装を指向した近似GMMに基づく未学習クラス推定ニューラルネット

Abstract

<p>General classification methods cannot treat unexpected patterns that are not considered in the training process. For this problem, our research group has proposed a probabilistic classification method that can classify abnormal patterns as the unlearned class. For the implementation of the classifier in the embedded hardware, this paper proposes a novel approximate Bayesian classification method with the anomaly detection based on Gaussian mixture models and the probabilistic density function of the unlearned class. The proposed classifier was applied to forearm motion classification in the experiments. Experimental results demonstrate the proposed method can achieve high classification performance as same as the previous model, and the effectiveness of the proposed method could be confirmed.</p>

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