搜索资源列表
knn
- 这个程序是用来寻找每个图像的K近邻的，然后得到了一个权值矩阵-This program is used to find K-nearest neighbor of each image, and then get a weight matrix
knn
- code of implementing the K-Nearest Neighbor method
papers
- These are knn algorithm documentations-These are knn algorithm documentations...
KNN4hypothroid
- this source code claculate KNN for hypothroid uci dataset
cvKNN
- KNN Classifier for a K population
Knn
- MATLAB 聚类和近邻搜索模块代码如下-MATLAB clustering and nearest neighbor search module code is as follows
knn
- knn用于分类的matlab源码，短小精悍-knn matlab source code for the classification, dapper
kNN
- KNN分类方法，用于文本分类工作的研究。-KNN classification method, for text categorization of the research work
knn
- knn matlab algorithm
KNN-matlab
- k近邻的两种改进算法，采用matlab实现，使得算法效率有了较大提高。-two methods for knn，both of them are run in matlat.
knn
- 最近邻算法(KNN)matlab代码实现，附数据集-Nearest Neighbor algorithm (KNN) matlab code, with data sets
KNN
- 一个KNN的matlab程序，供感兴趣的人士学习-A KNN matlab program for those interested in learning
基于用户的协同过滤推荐算法MATLAB
- 此代码为一个基于协同过滤的推荐算法，为基于用户的KNN协同过滤，由MATLAB实现，并计算MEA和RMSE
knn
- KNN分类器的MATLAB代码，速度快效果好，适合初学者使用。-KNN search without using any gancy data structure, such as kd-tree. However, it is the fastest knn matlab implementation I ever found.
kNN
- 一个用matlab实现的kNN机器学习算法，都是我自己写的，绝对原创-A machine learning algorithm using kNN matlab realize, is I wrote it myself, absolutely original
knn
- KNN matlab code download
KNN-matlab
- see matlab code for decision tree and knn algorithm , enjoy ,
KNN算法代码（matlab）
- 本实验中的KNN分类算法采用Matlab语言实现。 主函数“knnClass.m”读取训练和测试的数据文件，然后调用knn函数进行分类运算，并使用图像的形式将分类结果显示出来。(The KNN classification algorithm in this experiment is realized by Matlab language. The main function "knnClass.m" reads the training and test data fil
knnclassification
- KNN分类器，可用于信号分类，论文中使用过的(KNN classifier, can be used for signal classification, used in the paper)
KNN
- 在训练集中数据和标签已知的情况下，输入测试数据，将测试数据的特征与训练集中对应的特征进行相互比较，找到训练集中与之最为相似的前K个数据，则该测试数据对应的类别就是K个数据中出现次数最多的那个分类。(In the case where the training data and the tag are known, the test data is input, the characteristics of the test data are compared with the character