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无监督学习的英文

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"无监督学习"怎么读用"无监督学习"造句

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  • unsupervised learning

例句与用法

  • The former belongs to supervised learning and the latter belongs to unsupervised learning
    它们分属于有监督学习与无监督学习
  • Due to its unsupervised learning ability , clustering has been widely used in numerous applications , such as pattern recognition , image processing , market research and so on
    聚类具有无监督学习能力,被广泛应用于多个领域中,如模式识别、数据分析、图像处理以及市场调研等。
  • The supervised and unsupervised learning diagnosis methods are discussed and several improvements have been presented in the learning algorithms . the simulation results show that the proposed method can perforfti correct diagtioals iii the linear analog circuits with tolerances
    本文对模拟故障诊断的有监督学习和无监督学习方法分别进行了研究,通过对实现过程的分析,对经典的学习算法进行深入研究,并提出若干改进。
  • The main work includes : the research and conments about some recognition methods ; the research and comments about three kind of mathematics morphologic arithmetic ; clustering ; matlab embedded in the vb ; the difference analysis in the dynamic image and so on
    主要做的工作包括几种识别方法的研究与评述;三种数学形态学算法的实现以及各自的优劣比较;利用无监督学习进行聚类以及matlab在图像处理中的嵌入;动态图像的差分分析等。
  • In the respect of neural networks control for non - linear and uncertain system , a review of some available control strategy is made . combining neural networks control and conventional control strategy supervised learning , no supervised learning and reinforcement learning neural networks self - studying and adaptive control systems for ship course control are proposed . the thesis studies particularly their characteristics
    在非线性和不确定性系统的神经网络控制方面,论文总结了一些现有的神经网络自学习控制系统,然后将神经网络和常规控制(例如pid控制、自适应控制、内模控制等)结合起来,根据船舶操纵的特点,详细研究和分析了有监督学习、无监督学习和再励学习的船舶航向神经网络自学习型自适应控制系统。
  • 1 , q 3 , and at last prove the exisitence of ( q , m + n , n , m ) resilient functions when n > q ? 1 . intelligentized ids methods , which can make the system more adaptability and self - studying , are important research directions of ids so far . in order to make the ids systems have better identifying ability and efficiency against new intrusions , we propose the intrusion feature extra - ction algorithm based on ikpca by studying the different kinds of intrusion detection feature extraction algorithm based on unsupervised learning , and then theoretically analysis the conver - gence of the algorithm . in addition , we validate the validity of the algorithm by means of experim - ents ; at the same time , through studying ica and neural networks , we propose fastica - nn ids , and then test the kddcup99 10 % date set to make comparison of kpca 、 ikpca and fastica algorithms in intrusion detection advantages and disadvantages
    为了使入侵检测系统对新的入侵行为有更好的识别能力和识别效率,本文在研究了各种基于无监督学习的入侵检测特征提取方法的基础上,提出了基于增量核主成份分析( ikpca )的入侵检测特征提取方法,并对该方法进行了收敛性分析,同时结合仿真试验对其正确性进行了验证;另外,本文通过研究独立成份分析和神经网络,提出了基于快速独立成份分析和神经网络的入侵检测方法( fastica - nnids ) ,并通过对kddcup99的10 %数据集的检测比较了核主成份分析( kpca ) 、增量核主成份分析( ikpca )和快速独立成份分析( fastica )在入侵检测特征提取方面的优缺点。
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