some example source of java,for beginner.inside there are a lot of sample examples
標簽： beginner examples example inside
上傳時間： 2014-03-12
上傳用戶：yxgi5
This module defines safer C library string * * routine replacements. These are meant to make C * * a bit more safe in reference to security and * * robustness
標簽： replacements defines library routine
上傳時間： 2014-01-04
上傳用戶：佳期如夢
The ROSETTA C++ library is a collection of C++ classes and routines that enable discernibility-based empirical modelling and data mining, developed as part of my dissertation. A brief presentation of the library can be found therein.
標簽： discernibility-based collection routines ROSETTA
上傳時間： 2015-03-12
上傳用戶：開懷常笑
這是通過A*算法實現(xiàn)8數(shù)碼問題，求解可能出現(xiàn)的目標狀態(tài)。通過回溯的方法實現(xiàn)對A*算法。不同于其它狀態(tài)圖的搜索實現(xiàn)8數(shù)碼問題。
上傳時間： 2013-12-13
上傳用戶：jichenxi0730
A Practical Introduction to Data Structures and Algorithm Analysis，數(shù)據(jù)結構和算法，里面包括Java描述、C++描述和C描述三個版本，非常全
標簽： Introduction Structures Practical Algorithm
上傳時間： 2015-03-12
上傳用戶：fxf126@126.com
This a separate release of the OpenSS7 X/Open XTI/TLI library, TLI modules (timod, tirdwr) and the INET driver (inet) that provides Unix98 compatible interface to Linux NET4 TCP/IP stacks, and all the necessary manpages and other documentation. Although these components are contained in our LiS and Linux Fast-STREAMS releases, this tarball configures, builds and installs these components separate from those releases.
上傳時間： 2015-03-12
上傳用戶：mikesering
Pocket SIP Messenger is a SIP instant messaging client for small devices that use Windows CE (iPAQ Pocket PC, ...). The IM protocol is based on SIMPLE (IETF) working group s specification.
標簽： Messenger SIP messaging instant
上傳時間： 2015-03-13
上傳用戶：lyy1234
有關啟發(fā)式搜索的經(jīng)典算法：A*最短路徑算法的實例和對應程序。關注的朋友可以留意一下。（比傳統(tǒng)的Dijistra算法效率高很多哦！^_^）
上傳時間： 2013-11-28
上傳用戶：h886166
開發(fā)環(huán)境：maxplus2 a/d convortor
標簽： convortor maxplus2 開發(fā)環(huán)境
上傳時間： 2015-03-13
上傳用戶：asasasas
最新的支持向量機工具箱，有了它會很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.
上傳時間： 2013-12-16
上傳用戶：亞亞娟娟123
