Installer of grub as a floppy image.
標(biāo)簽： Installer floppy image grub
上傳時(shí)間： 2016-01-25
上傳用戶：2525775
Mod_python is an Apache module that embeds the Python interpreter within the server. With mod_python you can write web-based applications in Python that will run many times faster than traditional CGI and will have access to advanced features such as ability to retain database connections and other data between hits and access to Apache internals. A more detailed description of what mod_python can do is available in this O Reilly article.
標(biāo)簽： interpreter Mod_python mod_python the
上傳時(shí)間： 2016-01-25
上傳用戶：yd19890720
C寫的ArcSDE連接添加Feature的類
上傳時(shí)間： 2016-01-27
上傳用戶：huql11633
VB利用image控件進(jìn)行界面處理的例子。
上傳時(shí)間： 2016-01-27
上傳用戶：nanshan
an introduction of C Program by sunner sun of HIT .
標(biāo)簽： introduction Program sunner of
上傳時(shí)間： 2016-01-27
上傳用戶：星仔
Introduction Some times it is required that we build a shared library (DLL) from an m-file. M-files are functions that are written in Matlab editor and can be used from Matlab command prompt. In m-files, we employ Matlab built-in functions or toolbox functions to compute something. In my past articles, I showed you some ways to use Matlab engine (vis. API, C++ class or Matlab engine API) for employing Matlab built-in functions, but what about functions that we develop? How can we use them in VC? Is there any interface? This article shows you an idea to employ your own Matlab functions.
標(biāo)簽： Introduction required M-files library
上傳時(shí)間： 2016-01-29
上傳用戶：zhoujunzhen
An object based tree widget, emulating the one found in microsoft windows, | | with persistence using cookies. Works in IE 5+, Mozilla and konqueror 3.
標(biāo)簽： persistence emulating microsoft windows
上傳時(shí)間： 2016-01-30
上傳用戶：qiao8960
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.
標(biāo)簽： meta-learning classifiers combining Boosting
上傳時(shí)間： 2016-01-30
上傳用戶：songnanhua
it tells about how to use install shield to generate an installing file
標(biāo)簽： installing generate install shield
上傳時(shí)間： 2013-12-25
上傳用戶：徐孺
feature-selection-with-acquisition cost-for-optimizing-sensor-system-design.pdf
標(biāo)簽： feature-selection-with-acquisitio cost-for-optimizing-sensor-system design
上傳時(shí)間： 2016-01-30
上傳用戶：bruce
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