资源列表
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
decision-making-tree
- 决策树的Python代码实现，要所报内含原数据-Decision Tree Python code, containing the original data to be reported
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,
K-Means-master
- 模糊C均值聚类算法的PYTHON实现，在UCI的IRIS数据集上实现-Fuzzy C-means clustering algorithm PYTHON realization, implemented on UCI s IRIS data set
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
NB_All_Variables
- multi class naive bayes algorithm i coded for predicting football results
mixBern
- Just like EM of Gaussian Mixture Model, this is the EM algorithm for fitting Bernoulli Mixture Model. GMM is useful for clustering real value data. However, for binary data (such as bag of word feature) Bernoulli Mixture is more suitable.
BNT_SLP.tar
- Learning methods for Bayesian Networks and statistical tools
datato1ofm
- Take categorical data matrix and transform whole matrix to binary sparse 1ofM matrix, keeping track of what came where. Ideal for any form of count-based probabilistic analysis.-Take categorical data matrix and transform whole matrix to binary sparse