Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition...Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed.First,a small amount of labeled data are randomly sampled by using the bootstrap method,loss functions for three common deep learning net-works are improved,the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification.Subsequently,the dataset obtained after sam-pling is adopted to train three improved networks so as to build the initial model.In addition,the unlabeled data are preliminarily screened through dynamic time warping(DTW)and then input into the initial model trained previously for judgment.If the judg-ment results of two or more networks are consistent,the unla-beled data are labeled and put into the labeled data set.Lastly,the three network models are input into the labeled dataset for training,and the final model is built.As revealed by the simula-tion results,the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.展开更多
由于现有的多数概念演化检测方法本质上是基于监督学习,且通常用于解决一个时间段内仅出现一个新类,不能处理数据流中的类消失和类循环任务。为此,提出一种基于弱监督集成的概念演化自适应检测方法(AD_WE:Adaptive Detection Method for...由于现有的多数概念演化检测方法本质上是基于监督学习,且通常用于解决一个时间段内仅出现一个新类,不能处理数据流中的类消失和类循环任务。为此,提出一种基于弱监督集成的概念演化自适应检测方法(AD_WE:Adaptive Detection Method for Concept Evolution Based on Weakly Supervised Ensemble)。该方法利用弱监督集成策略构建集成学习器,对数据块中的训练样本进行局部预测,在此基础上,基于局部密度和相对距离识别特征空间中具有较强内聚性的相似数据并对其聚类,对聚类结果进行相似度比较,实现新类实例的检测及不同新类的区分;同时根据数据随时间变化特征建立动态衰减模型,及时消除消失类,并通过相似度比较检测循环类。实验表明,所提方法能对概念演化做出及时响应,可有效识别消失类和循环类,提高学习器的泛化性能。展开更多
文摘Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed.First,a small amount of labeled data are randomly sampled by using the bootstrap method,loss functions for three common deep learning net-works are improved,the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification.Subsequently,the dataset obtained after sam-pling is adopted to train three improved networks so as to build the initial model.In addition,the unlabeled data are preliminarily screened through dynamic time warping(DTW)and then input into the initial model trained previously for judgment.If the judg-ment results of two or more networks are consistent,the unla-beled data are labeled and put into the labeled data set.Lastly,the three network models are input into the labeled dataset for training,and the final model is built.As revealed by the simula-tion results,the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.
文摘由于现有的多数概念演化检测方法本质上是基于监督学习,且通常用于解决一个时间段内仅出现一个新类,不能处理数据流中的类消失和类循环任务。为此,提出一种基于弱监督集成的概念演化自适应检测方法(AD_WE:Adaptive Detection Method for Concept Evolution Based on Weakly Supervised Ensemble)。该方法利用弱监督集成策略构建集成学习器,对数据块中的训练样本进行局部预测,在此基础上,基于局部密度和相对距离识别特征空间中具有较强内聚性的相似数据并对其聚类,对聚类结果进行相似度比较,实现新类实例的检测及不同新类的区分;同时根据数据随时间变化特征建立动态衰减模型,及时消除消失类,并通过相似度比较检测循环类。实验表明,所提方法能对概念演化做出及时响应,可有效识别消失类和循环类,提高学习器的泛化性能。
