混合余事象分布に基づく未学習クラス推定確率ニューラルネット

抄録

General classification methods only involve consideration of learned classes, and do not cover undefined targets such as unintended characteristics in the learning process. This paper proposes a novel probabilistic neural network can treat unlearned class. The proposed method incorporates two types of probabilistic distribution: normal and complementary Gaussian distribution and can reach multi-class classification and unlearned class detection with a single network. In the experiments, artificial data and electromyogram (EMG) patterns were classified to demonstrate the capabilities of the proposed method. The results showed that the approach produces high performance for classification, and there were significant differences between the proposed and previous methods.