资源列表
bayes
- 首先对数据进行拆分，分为测试集与训练集，通过训练集进行贝叶斯网络的建模，最后利用建立的模型进行预测或分类任务的R语言代码-First, the data is split into a training set and test set, Bayesian network modeling through the training set, and finally the use of the model to predict or classify tasks R language code
IG
- 实现计算特征的信息增益，最后一列为样本标签，输出为特征的信息增益值及对应的序列号-Calculated to achieve gain characteristic information, and finally as a sample of tag output information characteristic of the gain value and the sequence number corresponding to
CHI
- 计算特征的卡方校验值，最后一列为样本标签，输出为特征的卡卡校验值及对应的特征序列号-The chi-square calculate checksum feature, the last one as a sample label, the output characteristics of the card verification value and the corresponding serial number feature
bayes
- 基于自然对数改进的朴素贝叶斯,统计TPR,NPR,TFR,TPR-Based on the natural logarithm improved Naive Bayes
openlogic-tomcat-7.0.12-windows-amd64-bin-1
- good coding for beginner
LogisticRegression
- 本例是用Python写的简单的逻辑回归的例子，可以下载试试。-This case is an example of a simple logistic regression written in Python, you can download a try.
SVM-introduction
- SVM浓缩讲稿，精简地介绍了svm的基本概念和理论，有助于新手理解和学习。-SVM speech introduces the basic principles and core concepts SVM to help the novice to learn and understand.
LassoBoosting
- lassoBoosting 自己实现的一个小程序，R语言版本的。比较简洁，适合理解这个算法-lassoBoosting own implementation of a small program, R language versions. Relatively simple for understanding the algorithm
bagging
- bagging算法的R语言实现，完整代码，运行速度较快-bagging algorithm R language, the complete code to run faster
LDA-topic-model
- 首先声明，这是别人写的LDA主题模型代码，本人测试过，可以运行，但是输出跟输出有点不尽人意，输入的是词的序号和该词在文档中出现的次数，要是可以直接读取文档就完美了。输出是主题以及词在该主题出现的概率，其中得到的主题我就看不懂了，不知道是算法问题，还是因为我的水平有限。在研究LDA主题模型的朋友，可以下载试一下-First statement, which is written by someone else LDA topic model code, I tested, you can run,
NaiveBayesClassifier.m
- I use Matlab 2008a which does not support Naive Bayes Classifier. scr ipt supports normal and kernel distributions. Statistics toolbox for 2008a version is used in the scr ipt. Also includes function for confusionmat
BayesClassify
- Requirements : 1) function fileOpen (user written) to open files : also uploaded 2)function strsplit1 also uploaded 3)training data, also uploaded