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weka-3-3-5
- own Java code. WEKA 是一个机器学习运算法则，它是为解决真实世界中的数据问题。它用java写成并且几乎可以在任何平台上运行。运算法则能够被直接应用到数据集上或者从你自己的java 码中调用。-own Java code. WEKA is a machine learning algorithms, it is to solve the real-world data. Using java can be written in almost any platform. Algor
mweka
- Matlab interface of weka
lmt
- this is code of data mining exatracted from weka tool which is java base
CorrelationSplitInfo
- this an weka tool source code implemented in java-this is an weka tool source code implemented in java
M5Base
- this an weka tool source code implemented in java used in dm-this is an weka tool source code implemented in java used in dm
RuleNode
- this an weka tool source code implemented in java in data mining purpose-this is an weka tool source code implemented in java in data mining purpose
SplitEvaluate
- this an weka tool source code implemented in java used to data mining evalution-this is an weka tool source code implemented in java used to data mining evalution
Impurity
- this is an weka tool source code implemented in java java impurity algorithm
DecisionTree
- this an weka tool source code implemented in java used for decision tr-this is an weka tool source code implemented in java used for decision tree
ID3Node
- this an weka tool source code implemented in java dm id3-this is an weka tool source code implemented in java dm id3
ReadTest
- this an weka tool source code implemented in java dm -this is an weka tool source code implemented in java dm
ReadTrain
- this an weka tool source code implemented in java-this is an weka tool source code implemented in java
sun
- this an weka tool source code implemented in java enjoy-this is an weka tool source code implemented in java enjoy
WekaInMatlab
- Use weka in matlab, call all the classifier in weka
matlab2weka
- Convinieng tool to use weka in matlab.
J48
- J48算法源代码,WEKA，C4.5算法源代码-J48 algorithm source code
Chameleon
- Chameleon聚类算法的Weka实现,实现一系列的算法，帮助更好的开发，实现。-Chameleon Weka clustering algorithm, has implemented a series of algorithms to help better develop, implement.
TP-Weka[1]
- a WEKA tp with NL and SVM learning methods
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
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