Description: 经典的人工智能问题 - iris数据分析问题。通过设计三层bp神经网络对花朵数据进行分类识别，并达到了很好的效果。-classic AI problem - iris data analysis problems. Three-bp through the design of neural networks classify data flower identification, and to achieve good results. Platform: |
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Author:lyx |
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Description: 经典的人工智能问题 - iris数据分析问题。通过设计三层bp神经网络对花朵数据进行分类识别，并达到了很好的效果。-classic AI problem- iris data analysis problems. Three-bp through the design of neural networks classify data flower identification, and to achieve good results. Platform: |
Size: 259072 |
Author:lyx |
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Description: 这是SPSS12统计分析教程（高级篇）(作者：张文彤)书中的有关数据-statistical analysis Guide (Advanced Part 1) (Author : Wen-Tong Zhang) of the data book Platform: |
Size: 406528 |
Author:moonweed |
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Description: 聚类分析isodata算法的C程序，实现对iris数据集分类的功能-ISODATA cluster analysis algorithm C procedures, to achieve the classification of iris data set features Platform: |
Size: 3072 |
Author:王璐瑶 |
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Description: eEye公司的网络数据分析软件，学习TCP/IP的得力助手-eEye s network data analysis software, learning TCP/IP of the right-hand man Platform: |
Size: 3814400 |
Author:许乐乐 |
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Description: IRIS数据 供大家分析参考 此数据可以用于聚类分析-U.S. analysis of IRIS data for the reference of this data can be used to cluster analysis Platform: |
Size: 8192 |
Author:zhangpeng |
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Description: 先用C-均值聚类算法程序，并用下列数据进行聚类分析。在确认编程正确后，采用蔡云龙书的附录B中表1的Iris数据进行聚类。然后使用近邻法的快速算法找出待分样本X（设X样本的4个分量x1=x2=x3=x4=6；子集数l=3）的最近邻节点和3-近邻节点及X与它们之间的距离。-First C-means clustering algorithm procedures and with the following data for cluster analysis. After confirming the correct programming, using the book蔡云龙Table 1 in Appendix B of the Iris data clustering. And the use of a close neighbor of law to be fast algorithm to find sub-samples of X (for X samples four components x1 = x2 = x3 = x4 = 6 subset of the number of l = 3) of the nearest neighbor nodes and 3- neighbor nodes and X and the distance between them. Platform: |
Size: 1024 |
Author:jack |
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Description: 使用分解聚类算法在IRIS数据上进行聚类分析，IRIS数据是由鸢尾属植物的三种单独的花的测量结果所组成，模式类别数为3，特征维数是4，每类各有50个模式样本，总共有150个样本。-The use of decomposition in the IRIS data clustering algorithm on the cluster analysis, IRIS data are from the iris flower three separate components of the measurement results, models for category 3, 4 are characteristic dimension, of each type of each 50 model samples, a total of 150 samples. Platform: |
Size: 3072 |
Author:liz |
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Description: 使用c-均值聚类算法在IRIS数据上进行聚类分析，随机选择三个初始聚类中心，经过多次迭代，最终将150个样本分为三类。-Use c-means clustering algorithm in the IRIS data on the cluster analysis, three randomly chosen initial cluster centers, through a series of iterative, 150 samples will eventually fall into three categories. Platform: |
Size: 3072 |
Author:liz |
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Description: WEAK 开发环境简介，介绍了WEKAWeka 试验（Experiment）环境可以让用户创建，运行，修改和分析算法试验，这会比单独的处理
各个算法更加方便。例如，用户可创建一次试验，在一系列数据集上运行多个算法（schemes），然后
2
分析结果以判断是否某个算法比其他算法（在统计意义下）更好。
可以通过Simple CLI 在命令行的方式下运行试验环境。例如，在CLI 上键入以下命令，将通过一
个基本的训练和测试步骤在Iris 数据集上运行OneR 算法。（注意该命令应放在同一行中输入CLI。）-WEAK development environment profile, introduced WEKAWeka test (Experiment) environment can allow users to create, run, modify and analysis algorithms test, which will deal with the various algorithms than the individual more convenient. For example, users can create an experiment in a series of data sets to run multiple algorithms (schemes), and then two results of the analysis to determine whether a particular algorithm than other algorithms (in the statistical sense) better. Simple CLI through the command line way to run test environments. For example, in the CLI, type the following command through a basic training and testing steps in the Iris data set to run on OneR algorithm. (Note that the order should be placed on the same line, enter CLI.) Platform: |
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Author:王春丽 |
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Description: 针对著名的UCI机器学习数据库中的iris data的kmeans聚类分析程序，具有代表性-For the well-known UCI machine learning repository of the iris data of kmeans cluster analysis procedure, a representative Platform: |
Size: 8192 |
Author:hannychen |
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Description: matlab聚类分析方法，利用c均值方法对IRIS数据进行聚类分析-matlab cluster analysis methods using c mean clustering analysis of IRIS data Platform: |
Size: 5120 |
Author:梅凯珍 |
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Description: 使用K－均值聚类算法在IRIS数据上进行聚类分析.-K-means clustering algorithm using IRIS data in the cluster analysis. Platform: |
Size: 3072 |
Author:李海 |
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Description: 使用c－均值聚类算法在IRIS数据上进行聚类分析-C-means clustering algorithm using IRIS data in the cluster analysis Platform: |
Size: 968704 |
Author:林帅 |
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Description: 用于svc分类， 用SMO实现SVC，并在UCI数据集Iris上进行实验,2. 可借鉴现成SVM软件包，实现回归分析。
-For svc classification, using SMO to achieve SVC, and in the UCI Iris data set on the experiment, 2. SVM can learn ready-made packages, to achieve regression analysis. Platform: |
Size: 3072 |
Author:陈德志 |
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Description: 分类是数据挖掘 、机器学习 和模式识别 中一个重要的研究领域。分类的目的是学会一个分类模型 (称作分类器)，该模型能把未知类别的数据项映射到给定类别中。目前发展较成熟的几种分类算法 如决策树、神经网络、贝叶斯方法、遗传算法等。分类具有广泛的应用，例如医学诊断、信用卡系统的信用分级、图像模式识别等。本毕业设计通过使用鸢尾属植物(IRIS)数据集，对当前数据挖掘中具有代表性的优秀分类算法进行分析和比较，总结出了各种算法的特性，为使用者选择算法或研究者改进算法提供了依据。-Classification of data mining, machine learning and pattern recognition is an important area of research. The purpose of classification is to learn a classification model (called classifier), the model can unknown types of data items mapped to a given category. Currently the more mature types of classification algorithms such as decision trees, neural networks, Bayesian methods, genetic algorithms. Classification with a wide range of applications such as medical diagnosis, credit card system, credit rating, image pattern recognition. The graduation project by using the Iris genus (IRIS) data sets, data mining has on the current outstanding representative of the analysis and comparison of classification algorithms, summed up the characteristics of various algorithms for researchers to improve the user selection algorithm or algorithm provided.
Platform: |
Size: 580608 |
Author:江霞 |
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Description: 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.) Platform: |
Size: 7168 |
Author:LLLLLL。.... |
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