资源列表
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)
BinaryNet-master
- 二值化程序代码，对数据进行很好地优化，实用于图像处理，图像去噪(Two value of the program code, the data is well optimized)
SVM
- 改程序可以实现基于支持向量机方法的径流中长期预报(A runoff forecasting method based on Support Vector Machines)
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)
kmediod
- k-mediod、knn、uci数据集。 数据挖掘、机器学习中的经典聚类、分类算法(K-mediod, KNN, and UCI data sets. Data mining and classical clustering and classification algorithms in machine learning)
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)
NBA_data_Analysis-Project
- 对NBA球员的大数据进行分析，含代码和结果图(Analyze the big data for NBA players, including code and result diagrams)
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)
deep_complex_networks-master
- 该存储库包含重现深层复杂网络文章中提供的实验的代码。(This repository contains code which reproduces experiments presented in the paper Deep Complex Networks.)
利用Python进行数据分析
- pandas库原作者编写，适用于初学数据分析者，主讲python的numpy、pandas、matplotlib库及如何进行数据分析(Pandas library original author, suitable for beginner data analyzer, speaker of Python's numpy, pandas, Matplotlib library and how to carry out data analysis)
GAM
- 主要利用R语言进行广义加法模型，进行回归预测(This paper mainly uses R language to carry on the generalized additive model, and carries on the regression forecast)
人工智能
- 从生物学开始到自然语言挖掘介绍人工智能，通识性的文档，对人工智能入门有极大的帮助。(From biology to natural language mining, the introduction of artificial intelligence, general knowledge documents, has a great help to the introduction of artificial intelligence.)