一個(gè)用Applet/Swing技術(shù)實(shí)現(xiàn)的簡(jiǎn)單記事本程序，可以作為初學(xué)者的入門教學(xué)演示程序，從中可以說(shuō)學(xué)習(xí)一些菜單操作方面的知識(shí)
標(biāo)簽： Applet Swing 技術(shù)實(shí)現(xiàn) 記事本
上傳時(shí)間： 2013-12-18
上傳用戶：ouyangtongze
這是一個(gè)用java編寫的文本編輯器,主要實(shí)現(xiàn)了部分簡(jiǎn)單常用的功能.其源碼在src文件夾下,本人發(fā)部此源碼主要目的是為了和所有喜歡java的朋友交流交流有關(guān)Swing方面的知識(shí)
上傳時(shí)間： 2013-12-23
上傳用戶：jiahao131
Java Swing 版的連連看，適合剛?cè)腴T的朋友學(xué)習(xí)，里面包含了一些算法，布局，通過(guò)閱讀代碼你或許有很大的收獲。
上傳時(shí)間： 2014-02-18
上傳用戶：csgcd001
Frequency Scale Conversion From f To f Scale frq2mel mel2frq mel The mel scale is based on the human perception of sinewave pitch. frq2erb erb2frq erb The erb scale is based on the equivalent rectangular bandwidths of the human ear. frq2midi midi2frq midi The midi standard specifies a numbering of semitones with middle C being 60. They can use the normal equal tempered scale or else the pythagorean scale of just intonation. They will in addition output note names in a character format.
標(biāo)簽： Scale Conversion Frequency mel
上傳時(shí)間： 2015-06-07
上傳用戶：
Based on 80C196MC One-chip Computer Frequency Conversion Rapid System of Accent Design Program Listing
標(biāo)簽： Conversion Frequency Computer One-chip
上傳時(shí)間： 2015-06-11
上傳用戶：我們的船長(zhǎng)
gaussian_model,color based segmentation,draw ellipse may used in face detection
標(biāo)簽： gaussian_model segmentation detection ellipse
上傳時(shí)間： 2015-06-13
上傳用戶：yuchunhai1990
color based segment in YCbCr space
標(biāo)簽： segment color based YCbCr
上傳時(shí)間： 2015-06-13
上傳用戶：84425894
一個(gè)關(guān)于java Swing的一些實(shí)例教程
上傳時(shí)間： 2015-06-14
上傳用戶：Late_Li
bayeserr - Computes the Bayesian risk for optimal classifier. ％ bayescln - Classifier based on Bayes decision rule for Gaussians. ％ bayesnd - Discrim. function, dichotomy, max aposteriori probability. ％ bhattach - Bhattacharya s upper limit of mean class. error. ％ pbayescln - Plots discriminat function of Bayes classifier.
標(biāo)簽： Classifier classifier bayeserr Computes
上傳時(shí)間： 2015-06-14
上傳用戶：sunjet
The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.
標(biāo)簽： algorithm Expectation-Maximization iterative optimi
上傳時(shí)間： 2015-06-17
上傳用戶：獨(dú)孤求源
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