合意形成による人工ニューラルネットワークの自己学習

—単純な実装に基づく訓練結果の分析—

抄録

Pattern recognition and event detection are essential technique for man-machine symbiosis. Supervised learning is one of the promising technique to achieve such ability, however gathering a large amount of training data is an extremely bothersome issue for its application. To tackle this problem, this study deals with self-training method which uses unknown input data for training with self-predicted output. This study particularly focuses on the self-prediction part in self-training, and proposes a novel self-prediction method based on collective modulation mechanism on consensus making. Through the numerical experiment with a naïve implementation, this report discusses the requirements and improvement points of the proposed mechanism.