搜索资源列表
knn_map
- 用得最多的是最近邻，此处上传的是K-近邻，即k＝1。matlab环境下的代码。附有实例。-used most often is the nearest neighbor, here is uploaded K-neighbor, k = 1. Matlab environment code. With examples.
PRAssign
- 脱机手写体识别Matlab源程序 包括特征提取、bayes分类器、K近邻分类及最近邻分类。 Testscr iptRecognition.m：测试代码 scr iptFeaExtract.m ：特征提取 KNearestEstimate.m ：K近邻估计 NearestEstimate.m : 最近邻估计 BayesTrain.m ：训练bayes分类器 Bayes.m ：测试bayes分类器 CrossValidate.m ：m交叉验证 -Offlin
knn
- 8个采样点的k近邻算法,结果用语言表示 两个类别
Parzen_KNN
- Parzen 窗 和 K近邻法进行概率密度估计 还带一个示波器控件.-Parzen window and K-nearest neighbor method probability density is estimated to bring an oscilloscope control.
knn.rar
- k近邻（knn）分类的文献，英文的，从IEEE下载得到，希望对大家有用,k neighbors (knn) classification of literature, English, and download from the IEEE has been the hope that useful for everyone
KNN
- K近邻算法（KNN）的matlab源代码，程序清晰易读-K nearest neighbor (KNN) of matlab source code, procedures legible
KNearestCls
- 模式识别中的K近邻法和快速K近邻法的VC＋＋实现-Pattern Recognition and rapid K neighbors K neighbors law VC to achieve
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
Knearestneighbor
- k近邻统计分类器对于大家做科研室很有帮助-kneighbor classfier
knn_demo
- k近邻算法(knn, k nearest neighbor)-k nearest neighbor (knn, k nearest neighbor)
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-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近邻法
- K近邻法对Iris数据分类，输入分类结果和准确率。-K-nearest neighbor method for Iris data classification, enter the classification results and accuracy.
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-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-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
NNC
- K近邻算法，亲测，可运行，手动输入近邻个数(The nearest neighbor algorithm)
kNN
- 通过k近邻算法实现数字识别，主要包含0-9之间的所有数字。(Digital identification is realized by K nearest neighbor algorithm.)