用vc實(shí)現(xiàn)Apriori算法的全部功能．請(qǐng)大家盡情心使用！-the algorithm can be used vc Apriori algorithm to achieve the full. Please rest assured use!
標(biāo)簽： algorithm Apriori the achieve
上傳時(shí)間： 2013-12-20
上傳用戶：ynzfm
this directory contains the following: * The acdc algorithm for finding the approximate general (non-orthogonal) joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices. [acdc.m] * The acdc algorithm for finding the same for a set of Symmetric matrices. [acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version [acdc.m], rather than the Symmetric version[acdc_sym] is preferable. * A function that finds an initial guess for acdc by applying hard-whitening followed by Cardoso s orthogonal joint diagonalizer. Note that acdc may also be called without an initial guess, in which case the initial guess is set by default to the identity matrix. The m-file includes the joint_diag function (by Cardoso) for performing the orthogonal part. [init4acdc.m]
標(biāo)簽： approximate directory algorithm the
上傳時(shí)間： 2014-01-17
上傳用戶：hanli8870
The kernel-ica package is a Matlab program that implements the Kernel ICA algorithm for independent component analysis (ICA). The Kernel ICA algorithm is based on the minimization of a contrast function based on kernel ideas. A contrast function measures the statistical dependence between components, thus when applied to estimated components and minimized over possible demixing matrices, components that are as independent as possible are found.
標(biāo)簽： independent kernel-ica implements algorithm
上傳時(shí)間： 2014-01-17
上傳用戶：yiwen213
he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
標(biāo)簽： equivalent likelihood algorithm Sejnowski
上傳時(shí)間： 2016-09-17
上傳用戶：Altman
qt-embedded開發(fā)的實(shí)例教程，通過10個(gè)實(shí)例讓你很快入門。
標(biāo)簽： qt-embedded 教程
上傳時(shí)間： 2013-12-17
上傳用戶：Altman
擴(kuò)展遺傳算法SPEAII(Strength Paretor Evaluation Algorithm)算法的代碼實(shí)現(xiàn),良好的程序框架，便于向其他應(yīng)用領(lǐng)域擴(kuò)展，建議大家使用。
標(biāo)簽： Evaluation Algorithm Strength Paretor
上傳時(shí)間： 2013-12-24
上傳用戶：cursor
embedded linux驅(qū)動(dòng)開發(fā) 嵌入式系統(tǒng)設(shè)計(jì)與實(shí)例 學(xué)做嵌入式Linux系統(tǒng)V0.1（完整版）
標(biāo)簽： embedded Linux linux 0.1
上傳時(shí)間： 2016-09-21
上傳用戶：ljt101007
Visual C++實(shí)現(xiàn)的基因遺傳算法庫源代碼以演示程序Free Source Code for Genetic algorithm 2008年05月21日 C++, Windows, Win32, Visual Studio, MFC, STL, Arch, Dev, Design 基因遺傳算法都是針對(duì)概率的，所以因?yàn)槠潆S機(jī)的本質(zhì)，導(dǎo)致其結(jié)果可能是好的，也可能是壞的，于是我們就需要一個(gè)方法確認(rèn)這個(gè)解到底有多大的可用性。這是通過計(jì)算相似擬合度進(jìn)行衡量的。染色體Chromosomes代表了基因遺傳算法的結(jié)果。每次迭代，算法生成一個(gè)染色體，這些子孫染色體又會(huì)產(chǎn)生新的迭代……關(guān)鍵內(nèi)容 這個(gè)基因算法庫是用 Visual Studio 2005 編寫的程序，第一個(gè)使用 Microsoft C/C++ 編譯器，第二個(gè)使用Intel C++ 編譯器。 如果你希望在你的程序你使用它，有兩個(gè)辦法，1是直接引用 Genetic Algorithm Library 項(xiàng)目，然后編譯；2是添加GeneticAlgorithm.lib 靜態(tài)鏈接庫到項(xiàng)目中
標(biāo)簽： algorithm Genetic Windows Visual
上傳時(shí)間： 2016-09-22
上傳用戶：silenthink
旺宏Flash Memory MX29LV320 driver. This program can for embedded system that using MX29LV320 (4MBytes) flash memory. User can porting to any RTOS by himself.
標(biāo)簽： 320 embedded 4MBytes program
上傳時(shí)間： 2016-09-26
上傳用戶：liglechongchong
Adaptive median filter using Embedded MATLAB
標(biāo)簽： Adaptive Embedded median filter
上傳時(shí)間： 2013-12-30
上傳用戶：924484786
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