搜索资源列表
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
IRIS
- 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
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)
iris
- 使用iris鸢尾花数据集测试rbf神经网络的分类效果(Using iris iris dataset to test the classification effect of RBF neural network)
MATLAB
- 鸢尾花识别程序，可以运行，望大神调整一下。(Iris flower recognition program,Can run,look great god adjustment.)
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)
鸢尾花用ID3算法
- 建立决策树模型基于R软件，利用DI3算法，利用鸢尾花数据(Establish a decision tree)
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)
MLP_iris
- 一个简单的多层感知器实现鸢尾花数据的分类的代码(use mlp to realize the classification of Iris dataset)
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.)
iris
- 文件包含鸢尾花csv数据集以及鸢尾花项目代码的py文件以及txt文件(The file contains the iris CSV dataset and the PY file and the txt file of the iris project code.)
NEWIRIS
- 在MATLAB下实现鸢尾花的分类，高效准确率高，备注详细(The iris classification is achieved under MATLAB, with high accuracy and high accuracy, and detailed remarks.)
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)
Iris-Dataset-Analysis-master
- 用决策树-回归分析模型来分析鸢尾花数据，训练后最终可以得到模型的准确率(Using decision tree-regression analysis model to analyze iris data, the accuracy of the model can be obtained after training.)
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)