搜索资源列表
events
- * acousticfeatures.m: Matlab scr ipt to generate training and testing files from event timeseries. * afm_mlpatterngen.m: Matlab scr ipt to extract feature information from acoustic event timeseries. * extractevents.m: Matlab scr ipt to extract ev
KNN11
- matlab有关K近邻分类算法，很不错，-matlab knn
svml_v092
- SVM,很好用的用于模式识别中特征分类的咚咚。-SVM, a good use for pattern recognition features of the classification of咚咚.
k-nn
- k-nearest neighbour classifier source code
KNN3
- matlab环境下 实现的3类分类的KNN算法-KNN algorithm to classify three classes developed by matlab
SVM
- SVM 神经网络 数据分类预测 葡萄酒种类识别-SVM neural network prediction wines identification data classification
knnknn
- matlab 的一个knn分类例子和数据-knn。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。
Classifier_K_NN_Mean_f
- matlab下，knn均值算法，可直接调用-matlab under, knn means algorithm, can be called directly
neighborhoodclassifier
- 这是基于KNN的邻域分类器matlab代码，里面包含了KNN的matlab代码。-This is a neighborhood based on KNN classifier matlab code, which contains the KNN of matlab code.
knnmatlab
- 不同的K近邻分类算法，基于matlab开发，不错。-matlab knn classification
KNNPandPneighborhoodPclassifier
- 模式识别knn分类器，matlab代码，k近邻算法-knn classifier ，matlab code
54738297KNN
- knn algorithm used to calcule K-nearest neightbourgh programmed on Matlab application
KNN_Matlab
- K近邻分类的Matlab算法，简单，适合初学，验证可用-KNN
knnclassifier
- knn是一种分类算法，它是基于matlab的一种模式识别的经典算法，一般适用于样本个数较小的样本分类。-KNN, it is classification algorithm based on matlab a pattern recognition of the classic algorithm, generally applied in sample number of smaller sample classification.
lab02
- 在MATLAB实现的用C实现的KNN算法，输入训练数据文件，和test文件，进行预测分类，并且可以与真实情况比较测试预测正确百分比-Implemented in MATLAB KNN algorithm implemented in C, the input training data file, and test files, to predict the classification and testing with the real situation is more correct pe
k-means-and-knn_matlab
- 用matlab实现机器学习中的两个经典算法：k-means和knn。-Using matlab to achieve in two classic machine learning algorithms: k-means and knn.
knnLLR
- matlab knn图构建以及局部线性重构加权方法-graph construction of KNNG and locally linear reconstruction for weighting the edges of knng