资源列表
LDA-matlab
- LDA智能算法，实现模式识别和聚类，例子是三维数据-LDA intelligent algorithm, to achieve pattern recognition and clustering, the example is three-dimensional data
k-means
- 简单实现聚类算法中的经典k-menans算法，实现数据是二维数据- U7B80 u5B5 u5B9 u7B0 u803A u7R09 u7B09 u7B09
nmf
- 非负矩阵分解，处理合成孔径雷达图像，数据处理(The multichannel or wide-angle imaging performance of synthetic aperture radar (SAR) can be improved by applying the compressed sensing (CS) theory to each channel or sub-aperture image formation independently.)
空间回归
- 检验数据在空间上分布是否具有规律性，并建立空间回归模型(The spatial distribution of the test data is regular, and the spatial regression model is established)
WS_net
- 复杂网络中小世界网络生成程序，可以用matlab生成制定聚类系数 连接概率和平均最短路径长度的经典小世界网络(A small world network generating program for complex networks, which can be used to generate a classical small world network with clustering coefficients, connection probabilities and average sho
直觉模糊C均值聚类
- 对所获取的数据进行无监督的直觉模糊C均值聚类(intuitionistic fuzzy C-means clustering)
xuanqufengzhi
- 一种天文图像的目标点快速检测算法的源程序。(A Fast Algorithm for Target Detection of Astronomical Images)
parse
- 根据除顿号意外的标点将评论预料进行分句处理(according to the punctuation to cut the comments into sentence)
SVD.m
- 利用SVD实现item-based CF: 优点： 简化数据，去除噪声，提高算法的结果 缺点： 数据的转换可能难以理解 适用数据类型： 数值型数据(Svd decomposition plays an important role in the decomposition of eigenvalues of high-dimensional data, while using low-dimensional data for approximate approximation)
Boosting
- 分类，用于对数据进行归类。把数据按照不同的属性进行归类，并且使归类的精确度越高越好。(Classification; used to classify data. Classify data according to different attributes, and make the accuracy of classification better.)
KNN，SVM，决策树，朴素贝叶斯
- 用python的sklearn包分类 简单的对数据进行分类(Sort with Python's sklearn package Simple classification of data)
统计建模于R
- 基于R语言的建模，结合例子的代码实现，包括假设检验与各种统计量的计算(Based on the R language modeling, combined with the code implementation of the example, including the hypothesis test and the calculation of various statistics)