资源列表
ICTCLAS_api
- 用于为指定文本进行分词操作。按照不同的词性进行分词。-Used to specify the text for the operation of word segmentation. According to different parts of speech.
optimization
- 用最少方差法对资源高峰进行优化,并以柱状图的形式直接显示出来结果-With a minimum variance method to optimize resource peak
Wavelet-Entropy
- 通过小波熵处理多年的降水量，求得最大值，最小值，并预测降水趋势-Through years of precipitation wavelet entropy process, and seek maximum, minimum, and predicted precipitation trends
SPSS
- spss数据分析软件教程，从入门到精通，里面含有实例-Simulation source codespss FP data analysis software, entry to the master
fix-kasusPpenduduk-DIY
- ARIMAX Transfer Function Models
findKN
- 在数据挖掘、人工智能等领域中，都常用到KD树来进行K近邻查找。本程序是自己用C++实现的一个KD树来进行的K近邻查找程序，包含建树和查找。文件中附有测试文件。-In data mining, artificial intelligence and other areas, it is commonly used to KD tree to find K nearest neighbor. This procedure is K neighbor Finder C++ they used to a
SMOTE
- 本工具为不平衡数据分类领域重要的过采样算法SMOTE.-This tool is an important field of unbalanced data classification oversampling algorithm SMOTE.
A-new_cluster_algorithm
- 2014年 6 月份，Alex Rodriguez 和 Alessandro Laio 在 Science 上发表了一篇名为《Clustering by fast search and find of density peaks》的文章，为聚类算法的设计提供了一种新的思路。虽然文章出来后遭到了众多读者的质疑，但整体而言，新聚类算法的基本思想很新颖，且简单明快，值得学习。-June 2014, Alex Rodriguez and Alessandro Laio on Science publis
pca11
- 使用matlab编程实现PCA算法，此算法经过测试，没有问题，可以放心使用-Use matlab programming PCA algorithm that has been tested, no problem, you can rest assured that use
ex5-003(Week6)_finished
- week6 百度大数据实验室NG的机器学习教程，包括文档以及代码，有些基本的分类聚类的机器学习算法，对于初学者很有帮助-week6 Baidu large data laboratory NG machine learning tutorials, including documentation and code, some basic clustering classification machine learning algorithms, very helpful for beginne
ex4-003(Week5)_finished
- week5 百度大数据实验室NG的机器学习教程，包括文档以及代码，有些基本的分类聚类的机器学习算法，很有帮助-week5 Baidu large data laboratory NG machine learning tutorials, including documentation and code, some basic classification machine learning clustering algorithm helpful
ex3-003(Week4)_finished
- week4 百度大牛NG的机器学习教程 文档以及程序代码，对于初学者很有帮助，有助于理解分类聚类等基本的机器学习方法-week4 Baidu Daniel NG machine learning tutorial documentation and program code, useful for beginners, help to understand the basic classification clustering of machine learning methods