搜索资源列表
DM_YeDan
- KNN(K最近邻)分类算法以及K-means(K均值)聚类算法是应用广泛的两种算法。本代码是在VS2010环境下，用 C++语言在基于KNN及K-means算法下，实现了对Iris数据集的分类与聚类。-KNN (K nearest neighbor) classification algorithm, as well as K-means (K mean) clustering algorithm is widely used two algorithms. The code VS2010 en
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.
Classification
- VC++中实现K近邻分类方法，实验数据是著名的iris数据库，此方法是数据挖掘，机器学习，人工智能等课程中重要的分类算法。-K-nearest neighbor classification VC++, experimental data is the famous iris databases, data mining, machine learning, artificial intelligence courses classification algorithm.
nn
- 最近邻算法实现 k近邻 Z为训练集，每行一个样本，n*m labZ为与Z对应的类别，列向量 Z_T为测试集，每行一个样本，p*m labZ_T为输出结果,p*1-Nearest-neighbor algorithm
Coordinate
- 基于R树的K近邻查询算法及递增的k近邻查询算法实现-the implemention of R-tree K-nearest neighbor query algorithm and incremental nearest neighbor query algorithm
knnsearch
- 一个小而有效的程序来执行的K近邻搜索算法， 非常适合对K -近邻搜索算法的入学者。-A small but effective procedures to implement the K-nearest neighbor search algorithm is very suitable for K- nearest neighbor search algorithm admission
classifier
- 用matlab实现Part1. 实现一个k近邻分类器，Part 2.实现一个最小二乘分类器，Part 3.实现一个支持向量机分类器，Part 4.在不同数据集上使用交叉验证选择各个算法的参数-Part1. Achieve a k-nearest neighbor classifier, Part 2. Achieve a least-squares classifier, Part 3. Implement a support vector machine classifier, Part 4.
lle
- LLE算法及改进的LLE算法，主要是对寻找K近邻时的欧式距离的改进-LLE algorithem and the improve_LLE agorithem，the process mainly improve the distance
KNN
- 机器学习K近邻分类算法，使用的是C++编程。如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别。-K-nearest neighbor classification machine learning algorithm, using the C++ programming. If a sample in feature space is k most similar (i.e., the feature space adjacent
6
- 最近邻法和K近邻法。作为分类器算法，k近邻法和最近邻法应用广泛-Nearest neighbor and K-nearest neighbor method. As a classifier algorithm, widely k nearest neighbor method nearest neighbor method and application
knn
- K近邻（KNN）：分类算法KNN是non-parametric分类器（不做分布形式的假设，直接从数据估计概率密度），是memory-based learning KNN不适用于高维数据（curse of dimension）-K-Nearest Neighbor (KNN): Classification Algorithm. KNN is a non-parametric classifiers (not to assume that the distribution of forms, fr
k_nearst
- k最近邻分类算法，将点云分为k近邻的算法，完整的源码程序-the program is about the k_nearst classify
knnalgorithm
- k最近邻算法，给出训练样本和测试样本，通过样本间欧氏距离或是绝对距离来寻找测试样本的k个近邻，并根据k个实例里多数所属的类将该测试样本归为该类。-k-nearest neighbor algorithm, given the training and testing samples by the Euclidean distance between the samples or the absolute distance to find the k nearest neighbors of th
static_K_ga01
- MATLAB代码，采用封装法利用K近邻和遗传算法的结合对数据进行分类-MATLAB code using encapsulation method using a K-nearest neighbor and genetic algorithm combined with data classification
ISOMAP-Algorithm
- ISOMAP算法，其中做了部分修改。算法采用K近邻图计算测地距离的方法，最后进行低维嵌入-ISOMAP algorithm, which made some modification.Algorithm of geodesic distance is obtained by using the K neighbor graph method, finally to low dimensional embedding
NN1akNN
- 实现机器学习中的最近邻算法——1-NN和k--Realization of machine learning algorithms 1-NN nearest neighbor and k-NN
knn
- k近邻法的线性扫描算法的python详细代码，并附有详细注释-k nearest neighbor linear scanning algorithm python code in detail, along with detailed notes
LMNN
- 大间隔最近邻居（Large margin nearest neighbor (LMNN)）分类算法是统计学的一种机器学习算法。该算法是在k近邻分类其中学习一种欧式距离度量函数。-Spaced nearest neighbor (Large margin nearest neighbor (LMNN)) classification algorithm is a statistical machine learning algorithms. The algorithm is learning a
knn
- K近邻分类算法实现 in Python -KNN Classfier in Python
Eigenface
- 人脸识别Eigenface算法的完整实现，主要基于PCA(主成成分分析)和kNN(k近邻)分类器实现，测试模板库基于ORL和yale，可以达到98 的识别率。-Eigenface complete recognition algorithm, mainly based on PCA (Principal Component Analysis into) and kNN (k nearest neighbor) classifier implementation, test template li