搜索资源列表
lotto2
- 搞活动用的抽奖小系统，加载名单，抽取特等，一等，二等，三等奖，并显式中奖信息。-Engage in activities with the sweepstakes small system load list, extract Principal, first, second and third prizes, and explicit winning.
Partial_Pivoting
- 利用matlab实现高斯列主元消去法解线性方程组的数值解。-Using matlab to achieve numerical solution of the principal component Gaussian elimination method for solving linear equations.
PCA-FCL
- pca for color space, Principal component analysis image proccesing for image color
herlin-project
- Abstract—In this study, we propose a novel approach for accurate 3-D organ segmentation in the CT scan volumes. Instead of using the organ’s prior information directly in the segmentation process, here we utilize the knowledge of the organ to v
fpca.py
- FUNCTION THAT CALCULATE THE PRINCIPAL COMPONENT ANALISIS PCA
pressf
- PLSR主成分因子确定的软件，输入自变量X和因变量Y即可得到结果-plsr principal component factor determining the software, enter the path to the variable x and the dependent variable y to get results
PCA
- PCA（主成分分析法）和ICA（独立成分分析法）的MATLAB源程序，他们是目前图像处理比较经典的特征提取方法-PCA (Principal Component Analysis) -matlab function of pca and ica of the signal
htglycx
- 一个合同管理源程序，具有数据库类型 access 公司合同号 项目编号 项目名称 项目性质 合同号 甲方 乙方 设备总价 合同总价 优惠百分比 优惠后总价 签约金额 报价时间 项目负责人 项目地区 签约人 这些项目。-A contract management source program, has the type of database access company contract number project Numbers project name project proper
Connect_Fo214822412009
- During his long sea voyages, Captain Cook was often absent in the evenings and eventually the crew began to joke that he must have a mistress in his cabin. When they discovered that the Captain had simply been playing this game with the ship s scien
pca1
- Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal component
qpsk-pca
- 对混合QPSK信号的主成份分析，实现混合信号的分离-The QPSK signal the principal component analysis
2011_7_1_49_56
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
859398
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
00913592
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
00941854
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
01262011
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
baker_simon_1999_3
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
pxc3876788
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
20130225
- 基于PCA主成分分析算法的人脸识别，包括Matlab代码及原理的PPT -Face recognition algorithm based on PCA principal component analysis, including Matlab code and the principle of the PPT
KPCA_SVM
- 核主成分分析和支持向量机方法相结合，用于数据分类和预测。-Kernel principal component analysis and support vector machine method combined for data classification and prediction.