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将维对分和K均值算法分割图像
- 利用聚类算法分割图像，将维对分法只可将图像分为2部分，可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数，聚类中心为随机值，当然也可以采用其他参数，程序编译为EXE文件后速度还可以接受，但尚有改进的余地，那位高手有空修改的话，请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can on
Detection_of_the_hidden_processes
- Many users have got used that Windows NT Task Manager shows all processes, and many consider that i s impossible to hide a process from Task Manager. Actually, process hiding is incredibly simple. There are lots of metho
Omu68K-Rtos.tar
- 用于motorala 68K系列处理器的小实时多任务操作系统 The OMU Kernel was written to provide a cut-down Unix-like O/S for a home-made 6809-based home computer built by me, Steven Hosgood, in the early 1980s. This package contains the RTOS
rbf_Kmeans
- 一个基于K均值聚类的ＲＢＦ神经网络，注释写的很明白，有不明白的地方可以发邮件问我。-a K-means clustering based on the RBF neural network, notes written very well, did not understand the local mail can ask me.
hacK
- * first open client.cpp and search for that USER_MSG_INTERCEPT(TeamInfo) over it u add this Code: USER_MSG_INTERCEPT(Health) { BEGIN_READ(pbuf,iSize) me.iHealth = READ_BYTE() return USER_MSG
MFY_kmeans
- 这是我帮一个本科生做的毕业设计，实现的数据挖掘的k均值和k中心算法，其中包含了我做的两个二维的数据集，感觉要预先知道k的参数值，不是很方便-This is what I do to help an undergraduate graduation Design, Implementation of the Data Mining mean k and k center algorithm, which includes me to do
Senfore_DragDrop_v4.1
- Drag and Drop Component Suite Version 4.1 Field test 5, released 16-dec-2001 ?1997-2001 Angus Johnson & Anders Melander http://www.melander.dk/delphi/dragdrop/ ------------------------------------------- Table of Co
MFY_kmeans
- 这是我帮一个本科生做的毕业设计，实现的数据挖掘的k均值和k中心算法，其中包含了我做的两个二维的数据集，感觉要预先知道k的参数值，不是很方便-This is what I do to help an undergraduate graduation Design, Implementation of the Data Mining mean k and k center algorithm, which includes me to do
将维对分和K均值算法分割图像
- 利用聚类算法分割图像，将维对分法只可将图像分为2部分，可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数，聚类中心为随机值，当然也可以采用其他参数，程序编译为EXE文件后速度还可以接受，但尚有改进的余地，那位高手有空修改的话，请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can on
Detection_of_the_hidden_processes
- Many users have got used that Windows NT Task Manager shows all processes, and many consider that i s impossible to hide a process from Task Manager. Actually, process hiding is incredibly simple. There are lots of metho
Omu68K-Rtos.tar
- 用于motorala 68K系列处理器的小实时多任务操作系统 The OMU Kernel was written to provide a cut-down Unix-like O/S for a home-made 6809-based home computer built by me, Steven Hosgood, in the early 1980s. This package contains the RTOS
rbf_Kmeans
- 一个基于K均值聚类的ＲＢＦ神经网络，注释写的很明白，有不明白的地方可以发邮件问我。-a K-means clustering based on the RBF neural network, notes written very well, did not understand the local mail can ask me.
12bitAD-K-typeThermocouple-max6675
- 12bitAD-ThermocoupleSensorIC max6675(工业级标准) 不好用你找我，绝对ok!-12bitAD-ThermocoupleSensorIC max6675 (industrial standard) not use your find me, absolutely ok!
k-mean
- K-means聚类算法的java实现描述！有详尽的说明，对初学者非常有用！-K-means Abduction呾Yang Jun珋mirror cavity java wife of mother
KWP2000_china
- 这是我整理的kwp2000通讯协议的物理层与数据链路层的中文文档，对于研究k线通信的同学有一定的指导作用-This is me organize the KWP2000 communication protocol physical layer and data link layer of the English documents, the study of k-line communication Student must have
K-meansNB
- ：将K—means算法引入到朴素贝叶斯分类研究中，提出一种基于K—means的朴素贝叶斯分类算法。首先用K— me．arks算法对原始数据集中的完整数据子集进行聚类，计算缺失数据子集中的每条记录与 个簇重心之间的相似度，把记 录赋给距离最近的一个簇，并用该簇相应的属性均值来填充记录的缺失值，然后用朴素贝叶斯分类算法对处理后的数据 集进行分类。实验结果表明，与朴素贝叶斯相比，基于K—means思想的朴素贝叶斯算法具有较高的分类
FlippingBook
- 1，完整的Flash杂志翻页效果控件源码 2，超多的各种效果演示，包括：右键菜单，URL点击，打印，动态创建杂志页，按键响应，翻页控件等等 3，包含完善的帮助说明 4，文档为英文版，需要中文的给我留言 QQ:30115970-1, the full effect of the Flash control source journal page 2, the effect of super-variety of pr
K
- 最近在学习一些数据挖掘的算法，看到了这个算法，也许k-均值算法对你来说很简单，但对我来说，是自己编写的一个算法。-Learn some data mining algorithms, see this algorithm, perhaps the k-means algorithm for you is very simple, but for me, I have written an algorithm.
J.H.-Conway---R.K.-Guy---The-Book-of-Numbers
- mira man y si me lo mamas en asterisco
rossler(k)
- rossler的轨迹图，不敢说多么有用，但是对于像我一样的初学者来说是非常有用的，简单明了！(rossler Rossler's track map doesn't say how useful it is, but it's very useful for beginners like me, simple and clear!)