搜索资源列表
moshishibie12345
- 模式识别，isodata 算法，对iris数据进行分类-pattern recognition, isodata algorithm, the iris data classification
iris.rar
- 数据挖掘经典专用UCI数据集，鸢尾花数据集,data mining dadaset
iris
- iris数据分类，bp神经网络实现，适当调整训练次数和训练精度，分类准确率可达99 以上-iris data classification, bp neural network, training times and training appropriate adjustments to the accuracy, classification accuracy rate of 99 or more
nearestneighboralgorithm
- 自己写的，模式识别最近邻算法实现，配有IRIS数据集，非常实用-Their own writing, pattern recognition, nearest neighbor algorithm, with IRIS data set, very useful
fcmgrnn
- 将FCM与广义回归网络结合起来实现对iris数据的分类-The FCM and generalized regression networks to achieve the classification of iris data
bbb
- 人脸识别是近年来模式识别、图像处理、机器视 觉、神经网络以及认知科学等领域研究的热点课题之 一。作为生物特征识别的一个重要方面, 人脸识别在 档案管理系统、安全验证系统、信用卡验证、公安系统 的罪犯身份识别、银行和海关的监控、人机交互等领域 具有广阔的应用前景。与指纹识别、视网膜识别、虹膜 识别等技术相比, 人脸识别技术在数据采集方面手续 比较简单, 使用者更容易接受。-Face recognition in recent years, pattern recogni
code
- 代码是MATLAB环境下基于k-nn算法的分类器对数据iris的分析-under matlab environment based on k-nn classifier for the iris.data
LDA
- LDA MATLAB的算法，可以加入iris数据，并且直接可以使用的-LDA MATLAB algorithm, can join the iris data, and can be used directly
Bayes-Iris
- 根据贝叶斯原理设计的一个简单的分类器，利用已知样本数据训练后，分类器就可以对未知样本进行分类。（实验时采用的是Iris数据集。）-According to the design of a simple Bayesian classifier, using the known training sample data, the classifier can classify the unknown samples. (Experiments using the Iris data set.)
BP_iris
- 应用bp网络对iris数据进行分类，得到了比较准确的结果-Application bp iris data classification network, get more accurate results
PCA
- 用pca分类器将Iris数据分开，并分析pca分类器的特点-Classifier with the Iris data pca separate and analyze the characteristics of classification pca
Graphics-using-SAS-iml
- Graphics using SAS/IML: Fisher’s Iris Data This code generate 4 scatter plots and prints them on a single page. Scatter plots of sepal length versus petal length, sepal width versus petal width, sepal length versus sepal width, and petal length ver
rdseedv5.3.tar
- rdseed，读去fullseed格式地震数据的软件，常用于iris下载后数据的读取及转化-rdseed, read go fullseed the format seismic data software commonly used in the iris after downloading data read and transformed
Cluster
- 对iris数据进行聚类，能够得到每类的具体数据信息-Clustering of iris data, can be specific for each type of data
IRIS-use-RFclassification
- 用随机森林RF方法分类IRIS数据集，用一百个数据做训练集，五十个作为测试集，并统计出错误率，可直接运行-Classification method with random forests RF IRIS data set, using one hundred data to do training set, and fifty as a test set, and the statistical error rate, can be directly run
MSE_Iris
- mse,Iris数据集，最小平方误差判别分类器-mse, Iris data collection, the least square error determination classifier
11
- We return to the iris data of the previous example to illustrate the Rand index, by comparing the k-means results with the true class labels. First we get some of the needed information to construct the matching matrix.
classification
- Iris数据的最近邻分类与k近邻分类程序，以及5路交叉验证，适合于新手学习，附有数据集-And nearest neighbor classification k-nearest neighbor classification procedure Iris data, as well as 5-way cross-validation, suitable for novices to learn, with data collection
IrisEye-Recognition-master
- zPreProcess ==> Proses Segmentasi Gambar Mata zFeatureExtract ==> Proses Ekstraksi Fitur Gambar Mata ZKlasifikasi ==> Proses Klasifikasi Mata Menggunakan Data Test dan Data Train
exp_2_1
- Iris数据集，计算协方差矩阵和相关系数矩阵和kl变换(The goal of this programming experiment is to: Calculate the covariance matrix and the correlation coefficient matrix of the Iris data set. Perform the Karhunen-Loeve transform on this data set.)