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时间:2018-11-12
《ect系统轮换对称svm图像重建改进算法》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、ECT系统轮换对称SVM图像重建改进算法 0引言2、brstyle="color:rgb(51,51,51);font-family:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;achines,SVM)作为机器学习的一个热点领域,具有良好的泛化能力以及较好的分类精确性,为ECT图像重建提供了有力的手段.3、t-size:16px;line-height:28px;-metricalpartitionsupportvectormachine)算法,并使其应用到ECT系统,采用CSPSVM算法,将大样本矩阵按ECT系统结构旋转对称分块,针对某一分块单元,从全局样本矩阵中选出适合这一单元的小样本组合,通过训练可得到最佳模型.4、tica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;fo5、nt-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;个样本,将其分为训练样本集和预测样本集.SVM通过训练样本集进行学习后,便可对预测样本集进行分类.6、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px7、;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;132,阈值为200.CSPSVM算法步骤如下:8、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;8.则管道截面n个单元水油分布组合为2,即可以有2个样本,而根据轮换对称性实际上不重复的样本数应为:
2、brstyle="color:rgb(51,51,51);font-family:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;achines,SVM)作为机器学习的一个热点领域,具有良好的泛化能力以及较好的分类精确性,为ECT图像重建提供了有力的手段.3、t-size:16px;line-height:28px;-metricalpartitionsupportvectormachine)算法,并使其应用到ECT系统,采用CSPSVM算法,将大样本矩阵按ECT系统结构旋转对称分块,针对某一分块单元,从全局样本矩阵中选出适合这一单元的小样本组合,通过训练可得到最佳模型.4、tica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;fo5、nt-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;个样本,将其分为训练样本集和预测样本集.SVM通过训练样本集进行学习后,便可对预测样本集进行分类.6、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px7、;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;132,阈值为200.CSPSVM算法步骤如下:8、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;8.则管道截面n个单元水油分布组合为2,即可以有2个样本,而根据轮换对称性实际上不重复的样本数应为:
3、t-size:16px;line-height:28px;-metricalpartitionsupportvectormachine)算法,并使其应用到ECT系统,采用CSPSVM算法,将大样本矩阵按ECT系统结构旋转对称分块,针对某一分块单元,从全局样本矩阵中选出适合这一单元的小样本组合,通过训练可得到最佳模型.4、tica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;fo5、nt-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;个样本,将其分为训练样本集和预测样本集.SVM通过训练样本集进行学习后,便可对预测样本集进行分类.6、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px7、;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;132,阈值为200.CSPSVM算法步骤如下:8、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;8.则管道截面n个单元水油分布组合为2,即可以有2个样本,而根据轮换对称性实际上不重复的样本数应为:
4、tica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;fo
5、nt-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;个样本,将其分为训练样本集和预测样本集.SVM通过训练样本集进行学习后,便可对预测样本集进行分类.6、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px7、;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;132,阈值为200.CSPSVM算法步骤如下:8、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;8.则管道截面n个单元水油分布组合为2,即可以有2个样本,而根据轮换对称性实际上不重复的样本数应为:
6、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px
7、;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;132,阈值为200.CSPSVM算法步骤如下:8、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;8.则管道截面n个单元水油分布组合为2,即可以有2个样本,而根据轮换对称性实际上不重复的样本数应为:
8、ht:28px;ily:微软雅黑,Helvetica;font-size:16px;line-height:28px;es;8.则管道截面n个单元水油分布组合为2,即可以有2个样本,而根据轮换对称性实际上不重复的样本数应为:
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