搜索资源列表
knn-classify
- 注释清楚，容易帮您理解k近邻分类的原理。采用c++语言编写-NOTE clear, easy to help you understand the principles of k nearest neighbors. Use c++ language
kNN
- KNN，k近邻算法，内附测试数据集，机器学习实战源码-KNN, k nearest neighbor algorithm, enclosing the test data set, machine learning practical source
KraskovMI
- k近邻互信息估计源代码,简单易用，可用于估计多维互信息 -kNN mutual information estimator by Kraskov
knn
- K近邻分类算法实现 in Python -KNN Classfier in Python
K-Nearest-Neighbor
- 数据挖掘中经典的KNN（K-最近邻）算法，导入即可运行-Data Mining the classical KNN (K- nearest neighbor) algorithm, you can import operation
mLknnMATLAB
- 本代码主要用了多标签K近邻方法（MLKNN）实现对多标签数据进行分类-This code mainly spent more than a label K-nearest neighbor method (MLKNN) to achieve the multi-label data classification
DM_BayesAndKNN
- C++实现数据挖掘的贝叶斯分类方法和k-近邻分类方法，有源码，有示例数据，有说明文档-C++ implementation of data mining methods and Bayesian classification k- nearest neighbor classification method, there is source code, sample data, documentation
KNN
- K近邻算法的python代码，针对婚恋网站调查数据-the python code especially for k-nearest algorithm on the condition of wedding data
KNN-implement-by-python
- 该程序是用python编写一个K近邻算法，通过该例子可以掌握K近邻算法，是学习数据挖掘的一个高效的算法。-The program is written in python a K-nearest neighbor algorithm, this example can grasp the K-nearest neighbor algorithm, a learning data mining and efficient algorithms.
kNN
- k近邻算法，可以简单准确的对数据，按照给定点与设定点的欧氏距离大小，进行分类-K nearest neighbor algorithm, can be simple and accurate data, in accordance with the set point to the point and the Euclidean distance of the set point size, classification
findKN
- 在数据挖掘、人工智能等领域中，都常用到KD树来进行K近邻查找。本程序是自己用C++实现的一个KD树来进行的K近邻查找程序，包含建树和查找。文件中附有测试文件。-In data mining, artificial intelligence and other areas, it is commonly used to KD tree to find K nearest neighbor. This procedure is K neighbor Finder C++ they used to a
K-means
- k-means简单实现，实现了k近邻的实现，以图像的形式显示出来，简单实用-k-means simple to achieve achieve a k neighbors realized and presented in the form of an image, simple and practical
最近邻分类代码
- 在linux 下C语言实现最近邻聚类算法，工程已经使用(near K neighbor cluster)
knn
- 模式识别中的k近邻算法，经过测试，运行结果很好。 最小距离分类器 : 它将各类训练样本划分成若干子类，并在 每个子类中确定代表点 。测试样本的类别则以其与这些代表点距离最近作决策。该方法的缺点是所选择的代表点并不一定能很好地代表各类，其后果将使错误率增加。(The k nearest neighbor algorithm in pattern recognition has been tested and the result is very good. Minimum distance c
PCA+mnist
- 基于python，利用主成分分析（PCA）和K近邻算法（KNN）在MNIST手写数据集上进行了分类。 经过PCA降维，最终的KNN在100维的特征空间实现了超过97%的分类精度。(Based on python, it uses principal component analysis (PCA) and K nearest neighbor algorithm (KNN) to classify on the MNIST handwritten data set. After PCA dime