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373901518KPCA
- 核函数主成分分析，用于数据的特征提取，对于训练样本的降维有较好的效果-Kernel principal component analysis, feature extraction for data, which can effectively reduce the dimension of training samples, the better
wwwroot
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mlclass-ex6
- 支持向量机，实现2或多分类，基于matlab仿真，内有说明-ex6.m- Octave scr ipt for the rst half of the exercise ex6data1.mat- Example Dataset 1 ex6data2.mat- Example Dataset 2 ex6data3.mat- Example Dataset 3 svmTrain.m- SVM rraining function svmPredict.m- SVM p
KPCA
- 核主成分分析KPCA算法，经过核变换将样本映射到线性可分的高维空间，再进行PCA降维。包括训练、测试、识别整个过程-KPCA kernel principal component analysis algorithm through nuclear transformation samples are mapped to linearly separable high-dimensional space, then PCA dimensionality reduction. Including
CHENGXU5
- 神经网络SVM实现分类，采用高斯核，标准差经过试验，最终定在0.81。训练和测试样本在1到1000之间间隔取点，训练样本取奇数，测试样本取偶数，没有噪声-SVM neural network to realize classification, USES the gaussian kernel, the standard deviation after test, final set at 0.81.Training and testing samples in the interval bet
ManifoldLearn
- 流形学习工具箱 ML_OPTIONS - Generate/alter options structure for training classifiers ML_TRAIN Trains a classifier with some options using some method ML_TEST Uses a classifier to classify data in X SAVECLASSIFIER Generates a classifier structu
matrbf
- 在MATLAB中调用核函数为rbf的svm，通过训练数据对测试数据进行分类（仅适用于二分类数据）(In MATLAB, use the kernel function rvf svm, through the training data on the test data classification (only for two categories of data))
LSSVMlabv
- trainlssvm函数用于训练模型，主要有两种使用形式： [alpha, b] = trainlssvm({X,Y,type,gam,kernel_par,kernel,preprocess}) model = trainlssvm(model)或者model = trainlssvm(model, X, Y) X和Y分别是训练样本集的输入和输出数据。(The trainlssvm function is used for training models, and there are t
KPCA故障检测程序（代码已优化）
- 基于核主元分析（KPCA）的工业过程故障检测，代码已优化，运行效率高，有详细的注释，附有训练数据和测试数据。(Achieves fault detection of industrial processes based on Kernel Principal Component Analysis (KPCA); the code has been optimized for high operational efficiency; detailed notes are attached with
Linux内核源代码情景分析(全册高清带书签)
- linux内核源代码情景分析，对于深入学习者有很大帮助(linux kernel program, it is very benifial to linux programmer for deeply learning or training.)
KRR
- 核岭回归算法 输入数据集(需要分开存放训练集和测试集) 利用4重交叉验证法调参 最后输出分类准确率(Kernel ridge regression algorithm Input data set (training set and test set need to be stored separately) Parameter adjustment by 4-fold cross validation Final output classification accuracy)