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Classify_Homework
- 模式识别作业——用平均样本法，平均距离法，最近邻法和K近邻法进行分类-pattern recognition operations -- with the average sample, the average distance, nearest neighbor and K-nearest-neighbor classification
knn_demo
- K近邻法的matlab程序，发现大家都在找它！-K-nearest neighbor method of Matlab procedures, I found that we all have to find it!
PatternRecognition
- 1.Fisher分类算法 2.感知器算法 3.最小二乘算法 4.快速近邻算法 5.K-近邻法 6.剪辑近邻法和压缩近邻法 7.二叉决策树算法
matlab K近邻分类法
- matlabK近邻法实现
K-近邻法
- 用于模式识别
2rar.rar
- 用matlab写的最近邻和K近邻法分类器，简单易懂，适合初学者,Written with matlab and K-NN nearest neighbor classifier, easy to understand for beginners
knn
- knn-K近邻法实现两分类的函数代码，输入为两类的样本特征，和待测试的样本向量，输出为分类结果。-knn-K nearest neighbor method to achieve the two categories of function code, enter the characteristics of two types of samples, and samples to be tested vector, the output for the classification.
KwithC-neighbor
- 用C语言对K近邻法进行的模式识别，包括说明及程序。-K with C-neighbor method of pattern recognition, including a descr iption of and procedures.
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
ClassifyHomework
- 模式识别,用平均样本法、平均距离法、最近邻法、K近邻法进行分类。-Pattern recognition, with an average of the sample method, the average distance method, nearest neighbor, K-NN classification.
zuijinlinfenlei
- 我们使用MATLAB软件实现了人脸识别并统计其识别率。本实验采用PCA（主成分分析）方法，利用K-L变换和奇异值分解原理实现。并分别采用最近邻法分类器得出它们的成功率。-We use face recognition software and the MATLAB Statistics recognition rate. The present study, PCA (principal component analysis) method, using KL transform and sin
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
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
KNN_demon
- 最近邻法语k近邻法的例子，基于matlab平台，有助于初学者学习。(The recent example of the nearest neighbour approach to French K, based on the MATLAB platform, helps beginners to learn.)
k-近邻点估计点云法向量
- 利用matlab实现k-近邻点估计点云法向量求解，(Matlab is used to solve the normal vector of k-nearest neighbor point cloud.)