搜索资源列表
kernelSVM
- 核方法＆svm是模式识别是很重要的方法，都各自成为一门学科-methods & svm is pattern recognition is very important way, it would become a respective disciplines
Kernel_Methods_for_Pattern_Analysis_Matlab_Tools_F
- 《模式分析的核方法》一书中的源代码，其中有该书的基本介绍和talk！
kdda
- 实现信号的KDDA映射变换，KDDA属于线性子空间分析方法LDA的改进算法，采用核方法实现映射
稀疏核方法
- 稀疏的核方法逼近算法，采用分组选择策略，速度很快。
kernel-density-estimation.rar
- 一种核密度估计，或者称作带宽选择的方法，可以估计二维尺度参数，至于多维以上的估计方法尚在开发，多维情况下个人经验好的方法是多次实验取较好值,kernel density estimation, bandwidth selection, two-dimensional scale parameter can be estimated ,for the multi-dimensional approaches are still under development, multi-dimensiona
Kernel_Methods_for_Pattern_Ana
- 该文档包含了描述核方法的经典书籍Kernel+Methods+for+Pattern+Analysis以及附带书中的源码，非常适合学习核方法的研究者，希望大家喜欢～,this document include the classic book Kernel+Methods+for+Pattern+Analysis which describs the kernel trick in detail, and with the souce code in it, hope you will like
parzen.rar
- 用parzen窗方法，估计概率密度，采用高期核函数。。。。,With parzen window means of estimating the probability density function using high nucleus. . . .
KDE
- 本文介绍了一种基于核函数的参数估计方法，对于初学者来讲很有帮助-kernel based estimation
SVMANDKERNELT
- 经典的SVM与核方法的,对研究SVM有帮助很好的说明SVM和核方法的PPT -Classical and nuclear methods of SVM, SVM for research that will help a very good method of SVM and nuclear PPT
learning_kernel_nonlinear
- 核方法用于解决非线性空间分类问题，这里面介绍的很详细-Methods to solve non-linear space for classification problems, there is described in great detail
Kernel_Methods_for_Pattern_Analysis
- 模式识别名著：核方法在模式识别中的应用，做模式识别的必看-Pattern Recognition masterpiece: Nuclear methods in pattern recognition application, do pattern recognition must-see
kernelbasedmoothing
- 基于核函数回归方法的图像去噪，图像平滑。对于图像领域的研究者有很大作用-Kernel regression method based on image denoising, image smoothing. Researchers in the field for the image plays a significant role
clustering
- 核方法用于聚类，方法获得了较好的效果，对于初学者有一定的帮助--this methods for clustering, methods to obtain good results, for beginners and has some help
kernelpca_tutorial
- Kernel pca基于核方法的特征提取算法-feature selection
face-recognition-algorithm
- 人脸识别 特征提取 代数特征抽取 子空间学习 投影寻踪 仿生模式识别 核方法-Face recognition feature extraction feature extraction algebra subspace projection pursuit learning pattern recognition of nuclear methods
Kernel-PCA
- 基于核方法的主成分分析matlab源代码，比较经典，推荐学习。-Method based on kernel principal component analysis matlab source code, more classic, recommended learning.
sparsekernelmethods_Code
- 稀疏核方法的逼近算法,Matlab实现，非常快速。-sparse kernel methods
BG
- 属于地球物理反演。不适定问题求解方法四，平均核方法。（有图像）-Belong to the geophysical inversion. Four ill-posed problem solving method, the average nuclear methods. (image)
核函数主成分分析KPCA
- 在多元统计领域中，核函数主成分分析(kernel principal component analysis, kernel PCA)是利用核函数方法技术对主成分分析(PCA)的扩展。使用核函数使原PCA的线性操作是在一个复制的内核希尔伯特空间中执行的。 KPCA的运算步骤势在PCA之前首先对数据进行kernel变换 ，再求相关系数矩阵。(In the field of multivariate statistics, kernel principal component analysis (ke
核Fisher鉴别分析方法（KFDA）
- 基于核函数的Fisher分类判别，用于比同种类的分类。(Fisher classification based on kernel function)