搜索资源列表
weka-3-4-4
- 由java开发的软件包,里面有人工智能所用的很多东东,包括神经网络,支持向量机,决策树等分类和回归分析方法,集成化软件哦!-by java development package, which has artificial intelligence used by many of the Eastern, including neural networks, support vector machines, such as decision tree classification and reg
projet
- 一个模拟weka的系统,输入文件格式和weka的一样,实现决策树的分析以及通过数据挖掘整理规则集合,很值得新手学习-a simulation system, the importation of files and weka, the same realization of the decision tree analysis and data mining collated by the rules set, is worth learning newcomers
Machine_learning
- Machine Learning with WEKA: An Introduction (讲义) 关于数据挖掘和机器学习的.-Machine Learning with WEKA : An Introduction (s) on the Data Mining and Machine Learning.
UCI
- 这是一个基于weka数据挖掘的实验测试数据集,格式为.arff,里面包含有名词性和数值型的数据集,用于weka挖掘测试.-This is a weka data mining based on the experimental data sets format. Arff. which contains terms and numerical data sets for test mining weka.
XRFFConverter
- Machine Learning Weka 数据变换,给Arff文件加载权值,变换为XRFF文件。
BPclassification
- BP学习算法应用——模式分类 应用动量BP学习算法对UCI提供的经典数据库——鸢尾属植物数据库进行分类,速度快,精度高。iris.arff为数据库文件,可用Weka数据挖掘软件打开。Iris.csv为源代码读取的数据文件,通过Weka软件转换得到。 将源文件Iris_classify.m和Iris.csv文件放入matlab的work文件夹中直接运行即可。
spider
- 马克斯普朗克提供的机器学习程序包,主要是matlab代码,另外也调用了大量的weka代码和libsvm代码
weka-3-4-12
- 基于Java及Mtlab联合应用的数据挖掘平台,可结合Spiker联合实现支持向量机的数据挖拙。
weka
- 这是有关weka的介绍 里面有关于分类 聚类 和数据预处理的类-This is the introduction to the inside on the weka classification class clustering and data preprocessing
数据挖掘
- 对于初学者学习weka这个数据处理的软件有用,arrf数据集(For beginners to learn Weka, this data processing software useful, arrf data set)
churn
- churn dataset for weka
NaiveBayesNLP
- 使用weka 运行朴素贝叶斯,去除拉普拉斯平滑(Use weka to run naive bayes, and delete laplace smoothing)
UCI
- 里面含有连续型数据集,离散型数据集以及混合型数据集可以用于属性约简,特征选择等算法的实验仿真。以及直接导入weka软件。(It contains continuous data sets, discrete data sets and mixed data sets, and can be used for the experimental simulation of attribute reduction and feature selection algorithms. And import
bayes
- weka中的贝叶斯分类算法,朴素贝叶斯,贝叶斯神经网络,文本分类(Bias classification algorithm in Weka, simple Bias, Bias neural network, text classification)
trees
- weka中的决策树分类算法,REPTree,RandomTree,RandomForest(Decision tree classification algorithm in Weka, REPTree, RandomTree, RandomForest)
clusterers
- 基于weka的聚类算法,简单聚类,SimpleKMeans,RandomizableSingleClustererEnhancer(Clustering algorithm based on Weka, simple clustering, SimpleKMeans, RandomizableSingleClustererEnhancer)
filters
- weka中的filter模块,包括监督学习和无监督学习(The filter module in Weka includes supervised learning and unsupervised learning)
FuzzyCMeans
- 在weka中添加fuzzycmeans算法的源码(Add fuzzycmeans algorithm source code in Weka)
FNNPSOGSA
- source weka with matlab
Classifiers
- 我们需要成百上千的分类器来解决现实世界的分类吗 我们评估179分类17种分类器(判别分析,贝叶斯,神经网络,支持向量机,决策树,基于规则的分类器,升压、装袋、堆放、随机森林和其他合奏,广义线性模型,线性,偏最小二乘法和主成分回归,logistic回归、多项式回归、多元自适应回归样条等方法),实现在WEKA,R(有或没有插入包),C和Matlab,包括所有目前可用的相关分类。(Do-we-Need-Hundreds-of-Classifiers-to-Solve-Real-World-Class