搜索资源列表
kde2d
- fast and accurate state-of-the-art bivariate kernel density estimator
my_function_1
- ica 核独立主元分析（kernel independent component analysis)软件包-kica
kda
- kda程序 用于matlab环境下不同核函数分类问题-kda program for matlab environment classification of the different kernel function
KDE
- Bivariate Kamma Kernel Density Estimate for large data set-optimize method
Kernel_PCA
- 基于核的主分量分析方法的提出者亲自写的程序（基于MATLAB-a MATLAB m-file of Kernel PCA
KPCA_p
- 核主成分分析中使用多项式核函数时的MATLAB代码，有注释，易看懂。-Kernel Principal Component Analysis in the use of polynomial kernel function of the MATLAB code, annotated, easy read.
svm
- SVM源代码程序，包含了SVM的各个子模块-SVM source code program, including the various sub-modules of the SVM
kerneladatron
- kernel adatron, svm impelemtation using gradient ascent method, fast and accurate for solving SVM problem with two classes
kerneladatron
- Kernel adatron, solving svm with gradient ascend method. fast and accurate.
kwiener
- The following code implements a kernel Wiener Filter algorithm in MATLAB. The algorithm dependes on the eigenvalue decomposition, thus only a few thousand of data samples for training dataset is applicable so far. -The following code implement
winerfilter
- The following code implements a kernel wiener filter algorithm in MATLAB.The algorithm dependes on the eigenvalue decomposition, thus only a few thousand of data samples for training dataset is applicable so far.
KRLS
- Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares
595643603713295726
- kfcm，为模糊核聚类算法，用于将低维的数据映射到高维进行分类，是较先进的算法-kfcm, the fuzzy kernel clustering algorithm for low-dimensional data is mapped to high-dimensional classification, is a more advanced algorithms
hehanshufcm
- 用Matlab实现基于核函数的C均值聚类图像分割，实验好，好用-Using Matlab implementation of kernel-based C-means clustering image segmentation, experimental is good, easy to use
pso-svm
- 这是一个用pso优化SVM中的惩罚参数C和核参数g的MATLAB源码，简单易学-This is an optimization of SVM with the pso in the penalty parameter C and kernel parameter g of the MATLAB source code, easy to learn
LWLR
- this program compare the Locally Weighted Linear Regression with three diferrent kernel function (gaussian, logistic basis, and Reciprocal Multiquadric) also compare locally weighted by simple Linear Regression.
Inertiadevicefaultpredictionbasedonwavelet
- ：为了提高最小二乘支持向量回归机的性能，将Morlet小波核函数引入其中，形成了最小二乘小波支 持向量回归机模型。利用待优化的参数重构模型的目标函数和约束条件，并在此基础上通过遗传算法进行参数 选择，从而提高了该模型的泛化能力。将最小二乘小波支持向量回归机应用于导弹陀螺仪的漂移趋势预测，仿真 实验结果表明了该方法的有效性和可行性，因此可以为陀螺仪的故障预报、可靠性辅助决策提供依据。-To improve the ability of least square support vect
SVM
- In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is in
KECA
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010. We introduce kernel entropy component analysis (kernel ECA) as a new method
KLFDA
- 基于局部Fisher准则的非线性核Fisher辨别分析，应用于有监督的特征提取与高维数据的有效降维。-Kernel Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction.