Depicting the associating degrees between two concepts and their relationships are major works for constructing a multi-relationship fuzzy concept network. This paper indicates some drawbacks of the existing methods o...Depicting the associating degrees between two concepts and their relationships are major works for constructing a multi-relationship fuzzy concept network. This paper indicates some drawbacks of the existing methods of calculating associating degrees between concepts, and proposes a new method for overcoming these drawbacks. We also use some examples to compare the proposed method with the existing methods for calculating the associating degrees between two concepts in a multi-relationship fuzzy concept networks.展开更多
流数据作为大数据的重要形式广泛存在于实际问题中,由于流数据中数据分布变化产生概念漂移,容易导致模型的泛化性能下降,且在实际应用问题中,数据标记成本较高,难以获得强监督的信息.针对以上问题,本文提出一种基于在线深度神经网络的...流数据作为大数据的重要形式广泛存在于实际问题中,由于流数据中数据分布变化产生概念漂移,容易导致模型的泛化性能下降,且在实际应用问题中,数据标记成本较高,难以获得强监督的信息.针对以上问题,本文提出一种基于在线深度神经网络的弱监督概念漂移检测(Weakly supervised conceptual drift detection method based on online deep neural network,WSCDD)方法.该方法设计了一种在线深度神经网络模型,采用Hedge反向传播方法在线学习网络深度,并通过设计Dropout层在模型预测时引入随机性,利用蒙特卡罗方法量化深度神经网络模型的预测不确定性,通过自适应滑动窗口技术检测弱监督环境下概念漂移的发生,并使模型适应新的概念.实验结果表明,该方法可以准确检测数据流中概念漂移的发生,在漂移发生后能够快速收敛到新的数据分布,提高了学习模型的泛化性能.展开更多
文摘Depicting the associating degrees between two concepts and their relationships are major works for constructing a multi-relationship fuzzy concept network. This paper indicates some drawbacks of the existing methods of calculating associating degrees between concepts, and proposes a new method for overcoming these drawbacks. We also use some examples to compare the proposed method with the existing methods for calculating the associating degrees between two concepts in a multi-relationship fuzzy concept networks.
文摘流数据作为大数据的重要形式广泛存在于实际问题中,由于流数据中数据分布变化产生概念漂移,容易导致模型的泛化性能下降,且在实际应用问题中,数据标记成本较高,难以获得强监督的信息.针对以上问题,本文提出一种基于在线深度神经网络的弱监督概念漂移检测(Weakly supervised conceptual drift detection method based on online deep neural network,WSCDD)方法.该方法设计了一种在线深度神经网络模型,采用Hedge反向传播方法在线学习网络深度,并通过设计Dropout层在模型预测时引入随机性,利用蒙特卡罗方法量化深度神经网络模型的预测不确定性,通过自适应滑动窗口技术检测弱监督环境下概念漂移的发生,并使模型适应新的概念.实验结果表明,该方法可以准确检测数据流中概念漂移的发生,在漂移发生后能够快速收敛到新的数据分布,提高了学习模型的泛化性能.