资源列表
GA-PLS-toolbox
- GA-PLS遗传偏最小二乘法 用于数据挖掘或者是光谱特征变量筛选-GA-PLS Genetic Partial Least Squares
PLS
- 偏最小二乘法（PLS）建模方法是集多元回归、典型相关分析及主成分分析的功能于一体的方法-Partial Least Squares
iPLS
- 区间偏最小二乘法（iPLS）筛选的特征光谱区域通过摒弃不相关的变量-Interval Partial Least Squares
prefixspan
- 经典序列模式挖掘算法prefixspan的python实现-Classic sequence pattern mining algorithm prefixspan
BinningandFeatSelection_GeneData
- Code to discretize and gene features. Works with numeric features with classes labeled as positive and negative-Code to discretize and gene features. Works with numeric features with classes labeled as positive and negative
NaiveBayes
- 贝叶斯分类器，机器学习十大经典算法之一，基本的实现-Naive Bayes
data-mining_GRI
- GRI关联数据挖掘. GRI是关联规则的一种算法。很有用的。-GRI association data mining
delete-character
- 任意输入字符串，把相同的字符清楚掉，剩下的字符按原先的排序组合在一起-Any input string, the same character clear out the rest of the characters are combined together in the original sorting
crawler
- python 爬虫爬取http://accent.gmu.edu/网站上的音频文件-Using python crawler to scape data the George Mason University Department of English Speech Accent Archive.
recommender-
- Collaborative Filtering，基于Collaborative Filtering，建立主动为用户推荐商品的推荐系统。实现参考协同过滤算法或它的优化，实现并改进算法，计算出每个客户对未购买的商品的兴趣度，并向客户主动推荐他最感兴趣的N个商品。实验数据可以从MovieLens.com下载。要求使用至少10，000不同用户的数据，至少1000个不同的movie。-Collaborative Filtering，Based Collaborative Filtering, the in
LOW-density-seperation
- 使用LDS(low density seperation)method 寻找最优分离面，然后对数据进行分类-Use LDS (low density seperation) method to find the optimal separation surface, and then classify the data
linear_CTSSVM
- 基于分段函数的支持向量分类机，使用BFGS算法进行求解-Piecewise-based support vector machine, using BFGS algorithm to solve