关键词: 主动学习, 自学习, 支持向量数据描述, 噪声源识别

Abstract: The majority of classification algorithm requires a large amount of labeled samples which are used to train the classifier model. In practical applications, samples which have been labeled are limited cause label them boring and expensive. The active learning and self-learning method based on uncertainty is introduced to the Support Vector Data Description（SVDD）, and a new classification method is proposed. Samples which have rich information to label them by experts are selecting through active learning, further, unlabeled sample set is used. The objective has good classification performance and saves the cost of labeling. The experimental results on identification of mechanical noise source of the submarine verify the performance of the proposed method.

Key words: active learning, self-learning, Support Vector Data Description（SVDD）, noise source identification