基于libsvm，開發的支持向量機圖形界面(初級水平)應用程序，并提供了關于C和sigma的新的參數選擇方法，使得SVM的使用更加簡單直觀.參考文章 Fast and Efficient Strategies for Model Selection of Gaussian Support Vector Machine 可google之。
標簽： libsvm
上傳時間： 2015-10-16
上傳用戶：cuibaigao
Notepad++ is a generic source code editor (it tries to be anyway) and Notepad replacement written in C++ with the win32 API. The aim of Notepad++ is to offer a slim and efficient binary with a totally customizable GUI
標簽： Notepad replacement generic written
上傳時間： 2013-12-18
上傳用戶：天涯
C++, although a marvelous language, isn t perfect. Matthew Wilson has been working with it for over a decade, and during that time he has found inherent limitations that require skillful workarounds. In this book, he doesn t just tell you what s wrong with C++, but offers practical techniques and tools for writing code that s more robust, flexible, efficient, and maintainable. He shows you how to tame C++ s complexity, cut through its vast array of paradigms, take back control over your code--and get far better results
標簽： marvelous although language Matthew
上傳時間： 2014-01-22
上傳用戶：妄想演繹師
This submission includes the presentation and model files that were used in delivering a webinar on 12-15-05 that covered the topic of modeling Hybrid Electric Vehicles. Hybrid electric vehicles (HEVs) have proven they can substantially improve fuel economy and reduce emissions. Because HEVs combine an electric drive with the internal combustion engine (ICE) in the powertrain, the vehicle?s kinetic energy can be captured during braking and transformed into electrical energy in the battery. The dual power source also means that the ICE can be reduced in size and can operate at its most efficient speeds.
標簽： presentation submission delivering includes
上傳時間： 2015-12-24
上傳用戶：zl5712176
Notepad++ is a generic source code editor (it tries to be anyway) and Notepad replacement written in C++ with the win32 API. The aim of Notepad++ is to offer a slim and efficient binary with a totally customizable GUI.
標簽：
上傳時間： 2015-12-28
上傳用戶：sjyy1001
This book shows how to design and implement C++ software that is more effective: more likely to behave correctly more robust in the face of exceptions more efficient more portable makes better use of language features adapts to change more gracefully works better in a mixed-language environment is easier to use correctly is harder to use incorrectly. In short, software that s just better.
標簽： more effective implement software
上傳時間： 2016-01-04
上傳用戶：huyiming139
Microsoft.NET測試驅動開發 Test-Driven Development in Microsoft .NET by James W. Newkirk and Alexei A. Vorontsov ISBN:0735619484 Microsoft Press © 2004 Using a wealth of pragmatic examples in C# and other .NET development tools, the two authors of this text demonstrate how to use automated tests to drive lean, efficient coding and better design.
標簽： Microsoft A. W. Development
上傳時間： 2014-01-07
上傳用戶：ynzfm
This m-file simulates MPSK (BPSK,QPSK,8PSK)with theoretical and simulated results using Gray coding. Numerical examples of a satellite link design are shown using QPSK and/or 8PSK when the bit rate(Rb)is greater than the channel bandwidth Wc (Band-limited channel).
標簽： theoretical simulates simulated results
上傳時間： 2016-01-19
上傳用戶：pompey
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.
標簽： meta-learning classifiers combining Boosting
上傳時間： 2016-01-30
上傳用戶：songnanhua
％ BackgroundRemoval=[true],false ％ Gain=[tsquare],linear ％ BandPass=[paul],fircls ％ CenterFrequency, auto (determined using pburg) ％ BandWidth=auto (a fraction of the CenterFrequency default=0.25) ％ ContrastStretch=[true],false ％ HilbertAmplitude=[true],false ％ HorizontalStacking=1 (a number of traces) ％
標簽： BackgroundRemoval CenterFrequen BandPass tsquare
上傳時間： 2016-03-04
上傳用戶：yyq123456789
