搜索资源列表
linjin
- 用k近邻法和剪辑近邻法分类样本点，模式识别实验内容之一-K neighbors with neighbors and editing sample points classification, pattern recognition one experiment
k-means
- K近邻算法，对一段数据进行分类，word 说明文档-K nearest neighbor algorithm
K-mean
- 最近邻分类器是一个用来聚类的算法，可以用来对iris数据进行聚类-k-means is a neanest alogorim
K-nearest
- 关于K近邻算法的详细描述，包括算法原理及应用背景。-K-nearest neighbor algorithm on a detailed descr iption, including algorithm theory and application background.
K-negibour-method
- 利用K近邻法实现数字识别算法。误差小，识别效率高，网络训练速度快。-K-nearest neighbor algorithm, digital identification algorithm. Error is small, high recognition efficiency and speed of network training.
k-iris
- 模式识别中用于完成数据的分类而用到的一种方法-k近邻。是将已有数据划分到3个类中，本方法中解决数据Iris数据的划分问题。将150个4维数据划分到3类。K近邻法是求最近的K个元素从而将其划分到已有类中。-Pattern recognition for the completion of the classification of the data used in a way-k neighbors. The existing data are divided into three classes
k-nearest-neighbor
- 对大量文件中相关内容的搜索，k近邻查询算法及一些改进-A large number of files in the search k nearest neighbor query algorithm and some improvements
K-
- 给出了一种基于距离的模糊隶属度定义形式，通过这种定义来改进K-近邻算法，得到一种自己定义的模糊K-近邻算法。同时本文中给出编程流程，编程思想以及实验结果。-A distance-based definition of fuzzy membership form, by this definition to improve K-nearest neighbor algorithm to obtain a defined fuzzy K-nearest neighbor algorithm. The
k-meas
- k近邻法分类iris数据。iris数据共分三类，每一类50个数据，这里把每一类前20个作为训练样本，后30个作为测试样本-k-nearest-neighbor classification iris data. iris data is divided into three categories, each category of data from 50, as the training samples in each category of the top 20 after 30 as th
K---nearest-neighbour-classifier
- 采用快速K近邻与Kmeans聚类算法来计算前K个近邻，舍弃了一部分不可能成为待测样本的前K个近邻的训练样本，从而减少了计算量，提高了分类速度-Fast K-nearest neighbor Kmeans clustering algorithm to calculate the K nearest neighbors, abandoning the training samples of the part can not become the first K neighbors of the t
K-nearest-neighbor
- K近邻估计方法，采用可变大小的小舱的密度估计方法。-K-nearest neighbor estimation method, the size of a small cabin with variable density estimation method.
k-neighbors
- k近邻，根据总样本确定一个参数k，即在总样本数为N时我们要求每个小舱内拥有的样本个数。-k neighbors, based on the total sample to determine a parameter k, ie, the total number of samples N, we require that each cabin has a small number of samples.
k-ball-fast-search
- 自适应空间球的k邻域快速搜索算法，利用空间微分块与动态球策略的k近邻搜索算法研究-Origianl-Adaptive spatial neighborhood k ball fast search algorithm, differential block the use of space and dynamic ball strategy k nearest neighbor search algorithm-Origianl
iris-k-nn
- Iris 是一种鸢尾属植物。在数据记录中，每组数据包含Iris花的四种属性：萼片长度，萼片宽度，花瓣长度，和花瓣宽度，三种不同的花各有50组数据. 这样总共有150组数据或模式。这里用K近邻法进行分类。-Iris is a genus Iris. In the data recording, the data containing each of the four attributes Iris Flower: sepals length, sepal width, petal length,
K-means-IRIS
- 用平均值取代表点的方法和K近邻法对Iris花进行分类-With the average of the representative point method and K-nearest neighbor to classify Iris flower
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
K近邻_KNN
- matlab的一份K邻近值KNN分类算法(A K neighbor value KNN classification algorithm for matlab)
image recognition
- 分别采用有边缘和无边缘两类图像作为样本库，使用用Hu不变矩对图像进行特征提取，并使用K近邻分类法进行分类和识别。(Two types of images with edges and no edges are used as the sample library, the images are extracted using Hu invariant moments, and the images are classified and identified by K nearest neighb
knn
- #k-近邻算法 实现 KNN 分类算法 # 对未知类别属性的数据集中的每个点依次执行以下操作： # (1)计算已知类别数据集中的点与当前点之间的距离 # (2)按照距离递增次序排序 # (3)选取与当前点距离最小的K个点 # (4)确定前K个点所在类别的出现频率 # (5)返回前K个点出现频率最高的类别作为当前点的预测分类(#k-Nearest Neighbor , KNN)
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)