sas教程 SAS数据挖掘技术的实现 作为智能型的数据挖掘集成工具，SAS/EM的图形化界面、可视化操作可引导用户（即使是数理统计经验不太多的用户）按SEMMA原则成功地进行数据挖掘，用户只要将数据输入，经过SAS/EM运行，即可得到一些分析结果。有经验的专家还可通过修改数据调整分析处理过程。
上传时间： 2016-08-20
上传用户：com1com2
把那个放大镜拖到mfc写的程序的窗口上，如securecrt,vc6等，松开鼠标就能看到一些内部函数地址了。如oninitdialog,onok什么的一目了然。拖到子窗口如铵钮上时，可以看出其id,当然，这个工作也可以由spy++完成。结合其父窗口message map的输出，还可以知道当点击这个按钮时，会跳到哪段程序上执行
上传时间： 2013-12-21
上传用户：zycidjl
基于J2ME的手机地图客户端源码，支持Google Map
上传时间： 2016-10-04
上传用户：cxl274287265
该代码不仅实现了编码的仿真，还在多种条件下实现了译码的仿真，包括MAP,LOG-MAP,SOVA下的单双滑动窗口。
上传时间： 2013-12-22
上传用户：钓鳌牧马
mani: MANIfold learning demonstration GUI by Todd Wittman, Department of Mathematics, University of Minnesota E-mail wittman@math.umn.edu with comments & questions. MANI Website: httP://www.math.umn.edu/~wittman/mani/index.html Last Modified by GUIDE v2.5 10-Apr-2005 13:28:36 Methods obtained from various authors. (1) MDS -- Michael Lee (2) ISOMAP -- J. Tenenbaum, de Silva, & Langford (3) LLE -- Sam Roweis & Lawrence Saul (4) Hessian LLE -- D. Donoho & C. Grimes (5) Laplacian -- M. Belkin & P. Niyogi (6) Diffusion Map -- R. Coifman & S. Lafon (7) LTSA -- Zhenyue Zhang & Hongyuan Zha
标签： demonstration Mathematics Department University
上传时间： 2016-10-29
上传用户：youmo81
This paper presents a visual based localization mechanism for a legged robot. Our proposal, fundamented on a probabilistic approach, uses a precompiled topological map where natural landmarks like doors or ceiling lights are recognized by the robot using its on-board camera. Experiments have been conducted using the AIBO Sony robotic dog showing that it is able to deal with noisy sensors like vision and to approximate world models representing indoor ofce environments. The two major contributions of this work are the use of this technique in legged robots, and the use of an active camera as the main sensor
标签： localization mechanism presents proposal
上传时间： 2016-11-04
上传用户：dianxin61
统计模式识别算法包，包括线性分类算法，SVM，PCA，LDA，EM，k-means分类等多种常用的模式识别算法。
上传时间： 2016-11-06
上传用户：stella2015
This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox
标签： i.e. re-implementations multi-instance different
上传时间： 2013-12-19
上传用户：haohaoxuexi
常见java数据结构的使用方法，包括Arrays类Collections类HashSet类List类TreeSet类Map类Vector类
上传时间： 2014-02-10
上传用户：qiao8960
This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to “see” around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors.
标签： simultaneously difficulties limitation addresses
上传时间： 2014-06-11
上传用户：waitingfy
