登录
首页 » matlab » sparsecoding

sparsecoding

于 2015-04-19 发布 文件大小:9001KB
0 230
下载积分: 1 下载次数: 411

代码说明:

  稀疏编码在图像分类中的实现,自己写的matlab程序,带demo(sparsecoding in image classification)

文件列表:

测试demo
........\Classification
........\..............\codeSCW.m,1780,2015-01-28
........\..............\display_network.m,2647,2015-01-28
........\..............\generateImages.m,651,2015-01-28
........\..............\getSparseCodingFeature.m,1416,2015-01-28
........\..............\Kits
........\..............\....\kit
........\..............\....\...\biggest.asv,264,2015-01-28
........\..............\....\...\biggest.m,271,2015-01-28
........\..............\....\...\distance.asv,784,2015-01-28
........\..............\....\...\distance.m,1283,2015-01-28
........\..............\....\...\gradi.m,540,2015-01-28
........\..............\....\...\kit01.asv,4574,2015-01-28
........\..............\....\...\localization.m,2722,2015-01-28
........\..............\....\...\mirror.m,320,2015-01-28
........\..............\....\...\preproc.m,1072,2015-01-28
........\..............\....\...\stretch.m,1934,2015-01-28
........\..............\....\...\test.asv,1879,2015-01-28
........\..............\....\libsvm
........\..............\....\......\libsvmpredict.mexw64,25600,2015-01-28
........\..............\....\......\libsvmtrain.mexw64,64512,2015-01-28
........\..............\....\minFunc
........\..............\....\.......\ArmijoBacktrack.m,3143,2015-01-28
........\..............\....\.......\autoGrad.m,807,2015-01-28
........\..............\....\.......\autoHess.m,901,2015-01-28
........\..............\....\.......\autoHv.m,307,2015-01-28
........\..............\....\.......\autoTensor.m,870,2015-01-28
........\..............\....\.......\callOutput.m,374,2015-01-28
........\..............\....\.......\conjGrad.m,1763,2015-01-28
........\..............\....\.......\dampedUpdate.m,953,2015-01-28
........\..............\....\.......\example_minFunc.m,2421,2015-01-28
........\..............\....\.......\example_minFunc_LR.m,1556,2015-01-28
........\..............\....\.......\isLegal.m,106,2015-01-28
........\..............\....\.......\lbfgs.m,885,2015-01-28
........\..............\....\.......\lbfgsC.c,2293,2015-01-28
........\..............\....\.......\lbfgsC.mexa64,7707,2015-01-28
........\..............\....\.......\lbfgsC.mexglx,7733,2015-01-28
........\..............\....\.......\lbfgsC.mexmac,9500,2015-01-28
........\..............\....\.......\lbfgsC.mexmaci,12660,2015-01-28
........\..............\....\.......\lbfgsC.mexmaci64,8800,2015-01-28
........\..............\....\.......\lbfgsC.mexw32,7168,2015-01-28
........\..............\....\.......\lbfgsC.mexw64,9728,2015-01-28
........\..............\....\.......\lbfgsUpdate.m,594,2015-01-28
........\..............\....\.......\logistic
........\..............\....\.......\........\LogisticDiagPrecond.m,397,2015-01-28
........\..............\....\.......\........\LogisticHv.m,208,2015-01-28
........\..............\....\.......\........\LogisticLoss.m,625,2015-01-28
........\..............\....\.......\........\mexutil.c,1111,2015-01-28
........\..............\....\.......\........\mexutil.h,309,2015-01-28
........\..............\....\.......\........\mylogsumexp.m,219,2015-01-28
........\..............\....\.......\........\repmatC.c,3816,2015-01-28
........\..............\....\.......\........\repmatC.dll,7680,2015-01-28
........\..............\....\.......\........\repmatC.mexglx,20682,2015-01-28
........\..............\....\.......\........\repmatC.mexmac,10000,2015-01-28
........\..............\....\.......\mchol.m,1228,2015-01-28
........\..............\....\.......\mcholC.c,3992,2015-01-28
........\..............\....\.......\mcholC.mexmaci64,13184,2015-01-28
........\..............\....\.......\mcholC.mexw32,8192,2015-01-28
........\..............\....\.......\mcholC.mexw64,12288,2015-01-28
........\..............\....\.......\mcholinc.m,539,2015-01-28
........\..............\....\.......\minFunc.m,42512,2015-01-28
........\..............\....\.......\minFunc_processInputOptions.m,3551,2015-01-28
........\..............\....\.......\polyinterp.m,4073,2015-01-28
........\..............\....\.......\precondDiag.m,41,2015-01-28
........\..............\....\.......\precondTriu.m,50,2015-01-28
........\..............\....\.......\precondTriuDiag.m,59,2015-01-28
........\..............\....\.......\rosenbrock.m,1074,2015-01-28
........\..............\....\.......\taylorModel.m,677,2015-01-28
........\..............\....\.......\WolfeLineSearch.m,11106,2015-01-28
........\..............\loadPictureControllor.m,468,2015-01-28
........\..............\localization.m,2720,2015-01-28
........\..............\model.mat,177,2015-01-28
........\..............\myClassification.fig,4342,2015-01-28
........\..............\myClassification.m,4331,2015-01-28
........\..............\myModel.mat,6894254,2015-01-28
........\..............\runClassification.m,852,2015-01-28
........\..............\sample.m,918,2015-01-28
........\..............\sparseCodingFeatureCost.m,3286,2015-01-28
........\..............\sparseCodingWeightCost.m,2504,2015-01-28
........\..............\trainSparseCoder.m,4823,2015-01-28
........\run.m,151,2015-01-28
........\test





........\....\daqiao1-5-20131026093928-20131026102213-65388271-6059.png,171894,2015-01-28
........\....\shengpingdasha-2-20131024041159-20131024082354-58950110-49404.png,189669,2015-01-28
........\....\shengpingdasha-2-20131024041159-20131024082354-58950110-65957.png,218416,2015-01-28
........\....\shengpingdasha-2-20131024041159-20131024082354-58950110-67527.png,230001,2015-01-28
........\....\shengpingdasha-3-20131024082358-20131024123551-58975429-207621.png,211586,2015-01-28
........\....\shengpingdasha-3-20131024082358-20131024123551-58975429-26023.png,209268,2015-01-28
........\....\shengpingdasha-3-20131024082358-20131024123551-58975429-61467.png,204731,2015-01-28
........\....\shengpingdasha-3-20131024082358-20131024123551-58975429-81009.png,195916,2015-01-28
........\....\shengpingdasha-4-20131024123557-20131024164756-58958908-35064.png,194160,2015-01-28
........\....\Untitled4.m,1457,2015-01-28
........\_运行run函数即可.txt,31,2015-01-28

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • Compound_Poisson_BenchASAA
    benchmarks several versions of code for generating Compound Poisson random variables
    2010-11-11 08:53:53下载
    积分:1
  • msvr
    实现多输出的支持向量机回归,不需要对每一个输出都进行函数回归(Multi-output support vector machine regression,Each output does not need to be a function of the regression)
    2013-11-29 10:43:56下载
    积分:1
  • LPG
    Plots of power and phase of two modes in transmission gratings(Long period gratings). both numerical and analytical methods
    2009-11-20 01:51:02下载
    积分:1
  • matlab1
    控制工具箱与SIMULINK软件应用:系统对象为G(s)=4/s(s+2) PI补偿器K1(s)=(s+1)/s K2(s)=(s+0.5)/s a 若由K1(s)与 G(s)串联组成的单位反馈系统,称为系统A。 b 若由K1(s) 放置在G(s)的反馈通道称为系统B。 c 若由K2(s)与G(s)串联组成的单位称为系统C,绘制上述每一种情况的阶跃响应与斜坡响应,求每一种情况下的系统阶跃响应误差与斜坡响应误差,并与理论分析结果进行比较。 (Control toolbox and SIMULINK software application: system object for G (s) = 4/s (s+ 2) PI compensator K1 (s) = (s+ 1)/s K2 (s) = (s+ 0.5)/s If a by K1 (s) and G (s) series of feedback system of the unit, known as a system a. B if the K1 (s) placed in G (s) feedback channel called system b. If c by K2 (s) and G (s) series of unit is called system c, draw the above each kind of condition of step response and slope response, for each of these cases system step response error and slope response error, and the results were compared with theoretical analysis.)
    2012-04-19 14:43:49下载
    积分:1
  • dcmotor
    dc motor simulation matlab
    2013-03-05 20:42:50下载
    积分:1
  • PhaSpaRecon
    主要用于计算相空间的源代码,程序是matlab代码,比较短小(computing chaos )
    2013-10-24 17:12:44下载
    积分:1
  • matlab
    logistic混沌产生过程,产生周期解的过程。(logistic chaos generation process, generating process of periodic solutions.)
    2013-11-27 22:26:53下载
    积分:1
  • floc-esprit
    在脉冲噪声情况下,基于分数低阶统计量的循环平稳信号的波达方向估计(In the case of impulse noise, DOA fractional lower order statistics of cyclostationary signal estimate)
    2015-06-03 10:54:58下载
    积分:1
  • power_3levelVSC
    matlab下的3电平VSC系统模型,对于初学者很有帮助,建议大家多多交流,互相帮助(The 3-level VSC system model under matlab is very helpful for beginners. It is recommended that you exchange more and help each other.)
    2018-07-31 12:41:41下载
    积分:1
  • Svm
    统计模式识别、线性或非线性回归以及人工神经网络等方法是数据挖掘的有效工具,支持向量分类(support vector classification,简称SVC)算法是一个很有发展前景的方向。(Statistical pattern recognition, linear or nonlinear regression and artificial neural network approach is an effective tool for data mining, support vector classification (support vector classification, referred to as SVC) algorithm is a promising direction.)
    2008-07-01 15:03:24下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载