This program demonstrates some function approximation capabilities of a Radial Basis Function Network. The user supplies a set of TRAINING points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the TRAINING samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
标签： approximation demonstrates capabilities Function
上传时间： 2014-01-01
上传用户：zjf3110
Blind Equalizer 的演算法主要是利用CMA及 LMS 的配合，当CMA将EYE打开，使讯号趋近于正确值，就切换到LMS，利用Slicer的输出当作TRAINING sequence来调整Equalizer的系数，而Carrier Recovery 的部份，则是将phase error track出来
上传时间： 2013-12-28
上传用户：it男一枚
C＋＋编写的机器学习算法 Lemga is a C++ package which consists of classes for several learning models and generic algorithms for optimizing (TRAINING) the models
标签： consists learning classes package
上传时间： 2014-01-21
上传用户：wangchong
一些数据库的实例。共12章。如第八章： 第8章数据库环境的建立 1. 用MISDBA用户登录MISDB数据库。 2. 在ISQL中，输入第8章提供的SQL语句；或者根据表8-1至表8-4在SQL Explorer中自行创建数据表。 3. 根据表8-5至表8-7设置初始数据，另外需要在PERSON数据表中设置一个具有培训管理系统管理权限的用户（ID=’PXC’，PASSWD=’PASSWORD’，AUTHORITY=’6’，STATE=’F’）和用于外派培训的用户（ID=’PXCOUT’，NAME=’外派培训’）。 4. 修改Admin源程序中的数据库连接属性，并且重新编译TRAINING.exe。 5. 修改Client源程序中数据库连接属性，并且重新生成html文件和cab文件，然后将这两个文件拷贝到web服务器指定目录中。
上传时间： 2014-01-09
上传用户：zxc23456789
pop3代理服务器源代码One of the most powerful features of Pop3 Agent is a naive Bayes filter, that is capable of recognizing spam e-mails after appropriate TRAINING. Pop3 Agent uses an embedded Firebird database server. Of course, you can configure Pop3 Agent to work with an existing server if there is another Interbase/Firebird installation available in your network. Open the Pop3 Agent home directory, delete or rename gds32.dll and ib_util.dll, and set the INI file parameter FBembedded=0.
标签： features powerful filter Agent
上传时间： 2014-01-10
上传用户：yoleeson
This code in this directory implements the binary hopfield network. Source code may be found in HOPNET.CPP. A sample TRAINING file is H7x8N4.trn. Sample test pattern files are: H7x8D4.TST, H5x8D7.TST, H5x8D7.TST and H5x8D9.TST, Output of the program goes to both the screen and a file, ARCHIVE.LST.
标签： code implements directory hopfield
上传时间： 2014-01-16
上传用户：123啊
palm编成,这种书很少,有兴趣看看 Title: Palm Programming: The Developer s Guide URL: http://safari.oreilly.com/JVXSL.asp?x=1&mode=section&sortKey=rank&sortOrder=desc&view=book&xmlid=1-56592-525-4&open=false&srchText=palm+programming&code=&h=&m=&l=1&catid=&s=1&b=1&f=1&t=1&c=1&u=1&page=0 ISBN: 1-56592-525-4 Author: Julie McKeehan/ Neil Rhodes Publisher: O Reilly Page: 478 Edition: 1st edition (December 1998) Catalog: PDA programming / Palm Format: pdf Size: 2.06M Supplier: Summary: Emerging as the bestselling hand-held computers of all time, PalmPilots have spawned intense developer activity and a fanatical following. Used by Palm in their developer TRAINING, this tutorial-style book shows intermediate to experienced C programmers how to build a Palm application from the ground up. Includes a CD-ROM with source code and third-party developer tools
标签： Programming Developer oreilly safari
上传时间： 2013-12-10
上传用户：litianchu
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of TRAINING images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
标签： approach combines particle tracking
上传时间： 2016-01-02
上传用户：yy541071797
Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) TRAINING algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models.
标签： Hidden_Markov_model_for_automatic speech_recognition implements left-right
上传时间： 2016-01-23
上传用户：569342831
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing TRAINING from millions of examples by hundreds of features in a reasonable time/memory.
标签： meta-learning classifiers combining Boosting
上传时间： 2016-01-30
上传用户：songnanhua
