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saomiao1
- 端口扫描工具,得到主机当前端口信息0~65535-A port scanner,gain the information of ports between 0 and 65535
2005621114504
- 本文详细讨论了添加到 RTC 的媒体改进特性,这些改进使得最终用户和开发者都能有更愉快的体验。当应用程序被构建在 RTC 客户端 API 之上,最终用户能获得丰富的音视频体验,而开发者可以使程序得到一系列免费的改进。使用这些 API 构建的应用程序还能够访问 RTC 提供的即时消息和出席功能。有关这些API的信息,可在 Windows Platform SDK中获得。 本文讨论了以下的特性和改进之处: 音频视频编解码器的可获得性 回波抵消(AEC) 冗余音频编码
MCU_teaching
- 不错的单片机方面的资料,里面既有教程,也有相应的汇编源代码,肯定会有所收获-good the information, both inside Guide, a compilation of a corresponding source code, it will definitely gain
MD5suanfaJavaBeanMD5
- MD5算法Java BeanMD5,希望大家参考学习,相信您会有所收获-Java BeanMD5 MD5 algorithm, we hope to learn information, I believe that you will gain
获得本地开放的端口
- 本程序是用C++builder写的 目的是获得本地开放的端口 [info] name=GetOpenPort ver= author= devtool=C++Builder sn= fullsource=1 desc=获取本机开放端口的信息 ╔-----------------------------╗ ┆ 欢迎访问 C++Builder 研究 ┆ ┆ Web: http://www.ccrun.com ┆ ┆ M
InformationGain
- 使用java实现的信息增益算法,附带了一些训练样本,已经进行了分词-Java algorithm using information gain realized, with some training samples have been carried out participle
c4.5
- C4.5是机器学习算法中的另一个分类决策树算法,它是基于ID3算法进行改进后的一种重要算法,相比于ID3算法,改进有如下几个要点:用信息增益率来选择属性.-C4.5 decision tree algorithm is another classification machine learning algorithm, which is based on ID3 algorithm is an important algorithm improved, compared to the ID3 a
InformationGain
- 全部信息熵的计算,包括信息熵,条件信息熵以及信息增益。附带Andrew的信息增益教程-All information entropy calculations, including information entropy, conditional entropy and information gain. Andrew incidental information gain Tutorials
TextureClassification_NonExtensiveEntropy
- Non-Extensive entropy with Gaussian Information Gain for identifying and classifying regular textures which contain repetitive patterns correlated over space that translates to high probabilities in the gray level co-occurrence matrix-Non-Extensive e
ID3
- MATLAB下的决策树ID3算法,应用信息增益来划分节点-ID3 decision tree algorithm under MATLAB application information gain to divide the node
columninformationGain
- 特征列的信息增益计算Java代码,比较好用,经过测试,效率较高-Information gain characteristic set of computing Java code, relatively easy to use, tested, high efficiency
treePlotter
- 决策树根据信息增益对数据进行分类,并且构造树的结构,输出结果易于理解-Decision tree based on information gain to classify the data, and construct the structure of the tree, the output result is easy to understand
informationgain
- 信息增益算法,该算法计算每个特征对数据集的信息增益。(Information gain algorithm, which calculates the information gain of each feature to the data set.)
GAFS
- 信息增益选取特征,遗传算法继续精简特征,效果较佳。(Information gain selection features, genetic algorithm to continue to streamline the characteristics of the better.)
DecisionTreeID3
- ID3算法是一种贪心算法,用来构造决策树。ID3算法起源于概念学习系统(CLS),以信息熵的下降速度为选取测试属性的标准,即在每个节点选取还尚未被用来划分的具有最高信息增益的属性作为划分标准,然后继续这个过程,直到生成的决策树能完美分类训练样例。(The ID3 algorithm is a greedy algorithm, which is used to construct a decision tree. ID3 algorithm originated from the concept
ig
- 代码是关于信息增益算法的,但是是基于数据库实现的算法。(The code is about the information gain algorithm, but it is based on the database implementation algorithm.)
决策树与随机森林
- 给出对决策树与随机森林的认识。主要分析决策树的学习算法:信息增益和ID3、C4.5、CART树,然后给出随机森林。 决策树中,最重要的问题有3个: 1. 特征选择。即选择哪个特征作为某个节点的分类特征; 2. 特征值的选择。即选择好特征后怎么划分子树; 3. 决策树出现过拟合怎么办? 下面分别就以上问题对决策树给出解释。决策树往往是递归的选择最优特征,并根据该特征对训练数据进行分割。(The understanding of decision tree and random
jiqixuexi
- 编写代码计算信息增益,splitDataSet函数是用来选择各个特征的子集的,比如选择年龄(第0个特征)的青年(用0代表)的自己,我们可以调用splitDataSet(dataSet,0,0)这样返回的子集就是年龄为青年的5个数据集。chooseBestFeatureToSplit是选择选择最优特征的函数。(Write code to calculate the information gain.SplitDataSet function is used to select the featur
php+mysql学生成绩管理系统-PH_qbvsc7
- 利用php实现,学生成绩管理系统,分三个模块:学生,教师和管理员。管理员模块:负责学生、老师信息的增删改;发布课程信息的增删改,以便让学生选课;审核老师提交的学生成绩并且打印成,可以作为初学者例子代码(Using PHP, student achievement management system, divided into three modules: students, teachers and administrators, the administrator module: respon
php+mysql学生成绩管理系统-PH_5fkc2z
- php脚本语言实现,学生成绩管理系统,分三个模块:学生,教师和管理员。管理员模块:负责学生、老师信息的增删改;发布课程信息的增删改,以便让学生选课;审核老师提交的学生成绩并且打印成,可以作为初学者例子代码(PHP scr ipting language implementation, student achievement management system, is divided into three modules: students, teachers and administrators