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根据鸢尾花多组数据先训练网络再对样本进行测试
- 基于BP神经网络，根据鸢尾花多组数据先训练网络再对样本进行测试，给出分类结果,Based on BP neural network, in accordance with multiple sets of data iris network before training again for testing classification results are given
nesors
- 神经网络VC实现,使用VC来实现bp神经网络，很好的实现了鸢尾花的数据分类，得到比较理想的结果。-bp network
bp
- 利用bp算法对鸢尾花数据进行分类的matlab实现程序-Bp algorithm using iris data classification procedures to achieve matlab
shenjing
- 使用bp神经网络进行分类。包括鸢尾花数据，以及训练过程和分类结果。包含非常详细的注释。-Use of bp neural network classification. Including the iris data, and training process and classification results. Contains very detailed notes.
iris
- 数据挖掘经典数据，鸢尾花分类，txt形式矩阵，直接使用非常方便。-Classical data mining data, iris classification, the form of TXT matrix directly, very convenient to use.
knn
- 基于KNN算法的鸢尾花分类代码，可一直在matlab上运行。-Iris classification code based on the KNN algorithm, has been running in matlab.
程序
- Fisher判别适合于两类的判别分析。本文采用的鸢尾花数据库中鸢尾花类别有三类，所以先采用Fisher判别对数据进行二分类判别分析，然后采用一对一进行多分类。(Fisher discriminant analysis is suitable for two kinds of discriminant analysis. There are three categories of iris in the iris database in this paper, so the Fisher disc
单层神经网络矩阵改进
- 基于Matlab编程，实现人工神经网络对经典三种鸢尾花数据的分类，利用Matlab矩阵运算的优势，对全部样本进行同时训练，具有很好的输出结果(An Artificial Neuron Network which accomplishes classifying three kinds of flower)
Iris_LR
- 根据已知的相关参数，鸢尾花逻辑回归分类效果(Based on the known relative parameters, the effect of logical regression on iris classification was studied)
fisher判别分析
- 利用fisher判别分析对于鸢尾花数据集进行分类(Fisher discriminant analysis was used to classify iris data sets)
BP神经网络-鸢尾花分类代码+文档
- BP神经网络-鸢尾花分类代码+文档，可以直接运行(BP neural network - iris code + document, can run directly)
logisitc regression
- 利用逻辑回归原理算法实现经典的鸢尾花分类问题(Using logistic regression algorithm to realize classical iris classification problem)
MATLAB实现鸢尾花数据集分类
- 基于BP算法的鸢尾花数据集分类，在MATLAB平台下编程实现BP算法，可计算识别率。(Based on the BP algorithm, iris data set is classified. Under the MATLAB platform, the BP algorithm is programmed and the recognition rate can be calculated.)
分类器评估及交叉验证_代码
- 内有鸢尾花数据的5折交叉验证实验代码，采用的分类器是贝叶斯分类器。(There is a 5-fold cross-validation experiment code for the iris data, and the classifier used is a Bayesian classifier.)
Matlab实现
- 使用两种不同神经网络预测鸢尾花的分类，对两种结果进行比较(Prediction of the classification of irises by two neural networks)
鸢尾花
- 封装KNN算法，了解IRIS数据集 分类鸢尾花数据集(Encapsulation of KNN algorithm to understand IRIS dataset classification iris dataset)
鸢尾花分类
- 使用四种方法进行鸢尾花分类：最小距离分类器，K 近邻法，感知器，Fisher 准则。(Four methods are used to classify iris: minimum distance classifier, K-nearest neighbor method, perceptron and Fisher criterion.)
有导师学习神经网络的分类-鸢尾花种类识别
- 有导师学习神经网络的分类-鸢尾花种类识别(Classification of Instructors Learning Neural Networks - Iris Species Identification)
鸢尾花 数据的处理
- MATLAB 利用Fisher分析和核Fisher分析对鸢尾花数据集进行分类，可以发现Kfisher 可以较好地对非线性数据的分类(MATLAB USES Fisher analysis and core Fisher analysis to classify the iris data set, and it can be found that Kfisher can classify the nonlinear data well)
Fishr集-鸢尾花分类源程序和相关文件
- IRIS SVM分类 初尝试 自带数据集(IRIS SVM CLASSIFICATION)