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OpenCV在VS2010下的配置

于 2020-04-02 发布
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代码说明:

说明:  OpenCV在VS2010下的配置ppt文件说明。(Description of configuration PPT file of OpenCV under VS2010.)

文件列表:

OpenCV在VS2010下的配置.pptx, 545282 , 2019-06-16

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    支持向量机分类训练算法SVM,它在解决小样本、非线性及高维模式识别中表现出许多特有的优势(SVM training algorithm )
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  • newpnn[1]
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    一种时频域联合捕捉主动声纳信号的方法.介绍了一种可应用于被动声纳的时频域联合捕捉主动声纳信号的方法及其工作流程。仿真相同噪声环境下使用该方法的发现距离与主动声纳探测距离,仿真结果表明使用该方法的发现距离远超过主动声纳探测距离,使用该方法可实现对目标的远程发现。(A time-frequency domain joint capture of active sonar signals. Introduced passive sonar, which can be applied when the frequency domain joint to capture active sonar signal its workflow. The discovery of the method used in the simulation of the same noise environment distance active sonar detection range, simulation results show that using the method found distance than active sonar detection range, the use of this method can be found remote target.)
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    - XCS for Dynamic Environments + Continuous versions of XCS + Test problem: real multiplexer + Experiments: XCS is explored in dynamic environments with different magnitudes of change to the underlying concepts. +Reference papers: H.H. Dam, H.A. Abbass, C.J. Lokan, Evolutionary Online Data Mining – an Investigation in a Dynamic Environment. 2005, accepted for a book chapter in Springer Series on Studies in Computational Intelligence H.H. Dam, H.A. Abbass, C.J. Lokan, Be Real! XCS with Continuous-Valued Inputs. IWLCS 2005, (International Workshop on Learning Classifier Systems). Washington DC, June 2005.(- XCS for Continuous Dynamic Environments Test versions of XCS problem : real multiplexer Experiments : XCS is explored in dynamic environments with di fferent magnitudes of change to the underlying concepts. Reference papers : H. H. Dam, H. A. Abbass, C. J. Lokan. Evolutionary Online Data Mining-an Investiga tion in a Dynamic Environment. 2005. accepted for a book chapter in Springer Series o n Studies in Computational Intelligence H.H. D am, H. A. Abbass, C. J. Lokan. Be Real! XCS with Continuous- Valued Inputs. IW LCS 2005. (International Workshop on Learning Classifi er Systems). Washington DC, June 2005.)
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