搜索资源列表
weka
- tspData <- read.csv( D:\\weka\\hw\\TSP.csv , header = T, sep = , ) #tspData <- `colnames<-`(tspData,c(1:8)) D <- as.matrix(tspData) tourLength <- function(tour, distMatrix) { tour <- c(tour, tour[1]) route <- embed(tou
TP-Weka[1]
- a WEKA tp with NL and SVM learning methods
roughset-into-weka
- 可以嵌入weka中的粗糙集约简算法，进一步扩充weka的数据挖掘功能-Weka can be embedded in the rough set reduction algorithm to further expand weka data mining functions
weka.pdf
- WEKA 中文API学习教程，详解 回归，分类，群集，最近邻算法-WEKA Chinese API tutorials, Detailed regression, classification, clustering, Nearest Neighbor algorithm
base-on-WEKA-text-cluster-apllying
- weka平台的文本分类测试，源代码为java-Text categorization test weka platform, the source code for the java
weka-new
- Weka的全名是怀卡托智能分析环境(Waikato Environment for Knowledge Analysis)，是一款免费的，非商业化(与之对应的是SPSS公司商业数据挖掘产品--Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called your own
weka-src
- 这是有关weka类的代码，里面介绍了关于分类 聚类的有关算法-It is about weka class code, which provides information on the classification of clustering algorithm
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
weka
- 机器学习调用weka的jar包实现的源码，包含朴素贝叶斯,决策树,ID3,以及特征选择的源码，数据集使用weka的数据集，需要使用arff文件读入。-Weka machine learning to call the jar package implements the source, including Naive Bayes, decision trees, ID3, and features selected source dataset weka data set, you need t
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)
weka-3-7-5
- weka 3.7.5 source code
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