搜索资源列表
knn.rar
- k-nn算法实现分类，模式识别作业，分三类，75个训练集，25个测试集，输出对测试集分类的结果,k-nn classification algorithm, pattern recognition operations are classified into three types, 75 training sets, 25 test set, the output of the test set classification results
parzen
- 这是一个模式识别中的parzen窗的一个简单仿真分类实例，其中female.txt和male.txt是训练样本，test.txt是测试样本，分类效果非常好，对于模式学习的初学者将会有很大帮助。-This is a pattern recognition in a simple window parzen Category simulation examples, one of female.txt and male.txt training samples, test.txt is the me
co-training
- 半监督学习co-training 回归算法的java代码实现。-COREG is a co-training style semi-supervised regression algorithm, which employs two kNN regressors using different distance metrics to select the most confidently labeled unlabeled examples for each other.
KNN
- 一个完整的KNN程序,含训练数据及测试数据-A complete KNN procedures, including training data and test data
knn
- K=5的K近邻分类，训练样本集含有30个样本,矢量长度为5-K = 5 K-neighbor classification, the training sample set containing 30 samples, vector length of 5
knnsearch
- KNN classifiers, training is training set, testing is test set, D is the distance, D=1 is mandistance D=2 is 欧氏距离 D=3是 infinite norm K is the number of selected neighbors- KNN classifiers, training is training set, testing is test set,
Counter
- KNN classifiers, training is training set, testing is test set, D is the distance, D=1 is mandistance D=2 is 欧氏距离 D=3是 infinite norm K is the number of selected neighbors- KNN classifiers, training is training set, testing is test set,
tsp_nn
- KNN classifiers, training is training set, testing is test set, D is the distance, D=1 is mandistance D=2 is 欧氏距离 D=3是 infinite norm K is the number of selected neighbors- KNN classifiers, training is training set, testing is test set,
knn
- In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space.
knn
- 本程序中,训练样本集含有30个样本,矢量长度为5,对样本{1,18,11,11,0.5513196}进行K=5的K-最近邻分类. 样本从文件data.txt中读取，程序运行结果显示所有样本以及其类别，待分类样本所属的类别（{1,18,11,11,0.5513196}属于"2"类），以及它的5个最近邻的类别和与它之间的距离。-In this program, the training sample set containing 30 samples, the vector length
lsvm-and-knn-elm
- lsvm and knn elm For training: elm_traing(TrainingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction) OR: [TrainingTime, TrainingAccuracy] = elm_train(TrainingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction) For te
KNN
- Implement the K nearest neighbor algorithm by your own instead of using available software. 2. Use K-fold cross validation to generate training and testing datasets. You should try different K values (3~8) to see how they affect your result. 3. T
KNN
- KNN算法分析及研究，将训练元组和测试元组看作是n维（若元组有n的属性）空间内的点，给定一条测试元组，搜索n维空间，找出与测试元组最相近的k个点（即训练元组），最后取这k个点中的多数类作为测试元组的类别。-Yuan KNN algorithm analysis and research, the training set and test set as a n d have the n properties (if a tuple) within the space of points, giv
KNN算法代码（matlab）
- 本实验中的KNN分类算法采用Matlab语言实现。 主函数“knnClass.m”读取训练和测试的数据文件，然后调用knn函数进行分类运算，并使用图像的形式将分类结果显示出来。(The KNN classification algorithm in this experiment is realized by Matlab language. The main function "knnClass.m" reads the training and test data fil
knn-MATLAB
- 这是一个实现简单的多数表决法的KNN算法。KNN算法涉及三个重要的步骤，分别是决定K的大小；距离的表达方法（一般有欧式距离，曼哈顿距离，Minkowski距离）；决策方法（多数表决法，KD树法等）。本程序是采用多数表决的决策方法，距离表达采用欧式距离。适用于小样本少特征的数据集。(KNN algorithm realized by MATLAB, useful for small training set and less features.)
KNN
- 在训练集中数据和标签已知的情况下，输入测试数据，将测试数据的特征与训练集中对应的特征进行相互比较，找到训练集中与之最为相似的前K个数据，则该测试数据对应的类别就是K个数据中出现次数最多的那个分类。(In the case where the training data and the tag are known, the test data is input, the characteristics of the test data are compared with the character
knn
- k最近邻算法:分类和回归。通过对训练集分类训练模型，验证集用于验证数据的准确性。(K nearest neighbor algorithm: classification and regression. Through the training set classification training model, the verification set is used to verify the accuracy of the data.)
KNN分类器
- 一、用python或matlab编写一个KNN分类器 训练集为semeion_train.csv（手写体识别） 测试集为semeion_test.csv 计算在测试集上错误率（k=1,k=3,k=5,k=10） ?(1. Write a KNN classifier with Python or matlab Training set is semeion_train.csv (handwriting recognition) The test set is semeion_test
KNN学习
- KNN学习,通过测试集和训练集进行预测KNN学习,(KNN learns to predict through test set and training set)
KNN
- 一个简单好用的KNN算法程序，只需要输入训练集和对应的标签就可以得到想要的模型并进行测试集的预测(A simple and easy-to-use KNN algorithm program only needs to input the training set and corresponding tags to get the desired model and predict the test set)