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DataMiningConceptsand-Techniques4
- 数据挖掘概念与技术——韩家炜 个人认为学习数据挖掘比较好的一般书,是美籍华人韩家炜教授著作,对算法讲解清晰-Data Mining: Concepts and Techniques Jiawei Han,Department of Computer Science, University of Illinois at Urbana-Champaign. website:www.cs.uiuc.edu/~hanj
DataMiningConceptsand-Techniques5
- 数据挖掘概念与技术——韩家炜 个人认为学习数据挖掘比较好的一般书,是美籍华人韩家炜教授著作,对数据挖掘涉及到的基础知识和算法讲解清晰-Data Mining: Concepts and Techniques Jiawei Han,Department of Computer Science, University of Illinois at Urbana-Champaign. website:www.cs.uiuc.edu/~hanj
DataMiningConceptsand-Techniques6
- 数据挖掘概念与技术——韩家炜 个人认为学习数据挖掘比较好的一般书,是美籍华人韩家炜教授著作,对数据挖掘涉及到的基础知识和算法讲解清晰-Data Mining: Concepts and Techniques Jiawei Han,Department of Computer Science, University of Illinois at Urbana-Champaign. website:www.cs.uiuc.edu/~hanj
DataMiningConceptsand-Techniques7
- 数据挖掘概念与技术——韩家炜 个人认为学习数据挖掘比较好的一般书,是美籍华人韩家炜教授著作,对数据挖掘涉及到的基础知识和算法讲解清晰-Data Mining: Concepts and Techniques Jiawei Han,Department of Computer Science, University of Illinois at Urbana-Champaign. website:www.cs.uiuc.edu/~hanj
DataMiningConceptsand-Techniques8
- 数据挖掘概念与技术——韩家炜 个人认为学习数据挖掘比较好的一般书,是美籍华人韩家炜教授著作,对数据挖掘涉及到的基础知识和算法讲解清晰-Data Mining: Concepts and Techniques Jiawei Han,Department of Computer Science, University of Illinois at Urbana-Champaign. website:www.cs.uiuc.edu/~hanj
DataMiningConceptsand-Techniques9
- 数据挖掘概念与技术——韩家炜 个人认为学习数据挖掘比较好的一般书,是美籍华人韩家炜教授著作,对数据挖掘涉及到的基础知 识和算法讲解清晰-Data Mining: Concepts and Techniques Jiawei Han,Department of Computer Science, University of Illinois at Urbana-Champaign. website:www.cs.uiuc.edu/~hanj
DataMiningConceptsand-Techniques10
- 数据挖掘概念与技术——韩家炜 个人认为学习数据挖掘比较好的一般书,是美籍华人韩家炜教授著作,对数据挖掘涉及到的基础知 识和算法讲解清晰-Data Mining: Concepts and Techniques Jiawei Han,Department of Computer Science, University of Illinois at Urbana-Champaign. website:www.cs.uiuc.edu/~hanj
DataMiningConceptsand-Techniques11
- 数据挖掘概念与技术——韩家炜 个人认为学习数据挖掘比较好的一般书,是美籍华人韩家炜教授著作,对数据挖掘涉及到的基础知识和算法讲解清晰-Data Mining: Concepts and Techniques Jiawei Han,Department of Computer Science, University of Illinois at Urbana-Champaign. website:www.cs.uiuc.edu/~hanj
test2012_5_7_16_18
- RADARSAT原始数据的cs成像算法,合成孔径雷达 -Cs imaging algorithm of the original RADARSAT data, synthetic aperture radar
ROMP
- 压缩感知的一种重构算法 recovery algorithm bases on cs-recovery algorithm
Construct-for-Compressed-Sening
- 一系列压缩感知(CS)的重构算法。 图像恢复-A series of compression-aware (CS) reconstruction algorithm. Image restoration
IMM_CS-CA-MCT
- 此程序为机动目标的交互多模型跟踪算法,采用了CS/CA/MCT模型-This program interacting multiple model for maneuvering target tracking algorithm, using the CS/CA/MCT model
CS_SP-algorithm
- cs压缩感知里的 SP算法,包含说明文档和程序文档-The cs compression perception of the SP algorithm, including documentation and program documentation
counter_striker
- 本源码实现了,CS地图里面的模型,手雷碰撞测试,另外附带了阴影的实现,采用的是Z-FAIL算法-z-fail algorithm
CSharp
- 适用于C#初学者的几个小程序,可以加深对这门语言的理解 包括有以下内容: 1.C#获取当前程序所在的文件夹.rar 2.C#界面皮肤(带例子).rar 3.C#如何使用托盘控件的实例源码.rar 4.C#文本加密解密算法示例源代码.rar 5.C#在开机时自动启动程序.rar 6.C#自定义皮肤.rar 7.CS聊天程序.rar 8.WPF模拟Windows+7气象源码.rar 9.玻璃按钮.rar 10.导出Excel格式.rar 11.导出ex
cs_explain_radar
- 压缩感知在雷达中的应用 很好的一个介绍 雷达成像中惯用的方法是匹配滤波,它之所以 能够处理低信噪比的问题,是因为它利用了回波数据的冗余信息。也就是目前雷达成像算法之所以成功的关键是具有足够 多的冗余信息。现在,在雷达成像中使用压缩感知恰好是反其道而行-Compressed Sensing(CS)theory is a great breakthrough of traditional Nyquist sampling theory,it accomplishes cornpressive
Wavelet_IRLS
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为ILRS算法,对256*256的lena图处理,比较原图和IRLS算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_OMP
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为OMP算法,对256*256的lena图处理,比较原图和OMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_SP
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为SP算法,对256*256的lena图处理,比较原图和SP算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and
Wavelet_ROMP
- 压缩感知CS——采用小波变换进行稀疏表示,高斯随机矩阵为观测矩阵,重构算法为ROMP算法,对256*256的lena图处理,比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果,并各运行50次,比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matr