登录
首页 » matlab » work

work

于 2013-05-22 发布 文件大小:7617KB
0 235
下载积分: 1 下载次数: 3

代码说明:

  使用matlab自己编写的改进的lda代码(lda use matlab,i do it by myself)

文件列表:

work
....\array_to_vecter.m,938,2008-05-08
....\a_terminal_caculus.m,189,2009-02-22
....\center_arrange_caculus.m,760,2009-02-20
....\center_class_caculus.m,329,2009-02-20
....\chinesetitlelabel.m,1465,2009-03-14
....\Collect.m,12609,2009-01-05
....\createaxes.m,1079,2012-11-09
....\digit2.mat,1281792,2008-05-28
....\digit3.mat,409064,2009-01-05
....\dismost_class_caculus.m,671,2009-02-17
....\distance_caculus.m,158,2009-01-05
....\dis_center_caculus.m,279,2009-02-17
....\DW.m,138,2008-05-13
....\dwt_function.m,120,2009-01-05
....\englishtitlelabel.m,1972,2009-03-12
....\insert.m,392,2009-02-18
....\Judge_a_terminal_caculus.m,2375,2009-02-22
....\kernal_recognize_caculus.m,1008,2009-01-05
....\kernal_vecter_caculus.m,1009,2009-01-05
....\kernal_Weight_caculus.m,242,2009-01-05
....\main.m,1405,2013-04-06
....\max_d_same_caculus.m,777,2009-01-05
....\min_d_difference_caculus.m,810,2009-01-05
....\min_d_two_class.m,496,2008-05-28
....\my_Spread_Difference_caculus.m,836,2009-01-05
....\my_Spread_inner_caculus.m,778,2009-01-05
....\non_a_optimal_afternumber_caculus.m,290,2009-01-05
....\non_a_optimal_caculus.m,1228,2009-01-05
....\non_zero_vecter_caculus.m,1100,2009-01-05
....\pair_Difference_caculus.m,414,2009-02-22
....\PCASpread_Difference_caculus.m,404,2009-02-25
....\PCASpread_inner_caculus.m,137,2009-02-25
....\photo_w_reflect_caculus.m,877,2009-01-05
....\Processing.m,9840,2009-03-11
....\randcollect.m,361,2009-02-22
....\rand_int.m,421,2009-02-22
....\rate_recognize_caculus.m,694,2009-01-05
....\Reflect_vecter_num_caculus.m,498,2009-01-05
....\Samplenumberprocessing.m,737,2009-02-26
....\select.m,836,2012-06-11
....\shortest_distant_caculus.m,1174,2009-01-05
....\Spread_Difference_caculus.m,1015,2009-01-06
....\Spread_inner_caculus.m,985,2009-01-06
....\subject0101.centerlight,35413,1997-04-07
....\subject0102.glasses,36294,1997-04-07
....\subject0103.happy,36692,1997-04-07
....\subject0104.leftlight,42921,1997-04-07
....\subject0105.noglasses,34100,1997-04-07
....\subject0106.normal,36129,1997-04-07
....\subject0107.rightlight,71627,1997-04-07
....\subject0108.sad,36370,1997-04-07
....\subject0109.sleepy,36307,1997-04-07
....\subject0110.surprised,36647,1997-04-07
....\subject0111.wink,36381,1997-04-07
....\subject0201.centerlight,31137,1997-04-07
....\subject0202.glasses,45239,1997-04-07
....\subject0203.happy,43765,1997-04-07
....\subject0204.leftlight,38159,1997-04-07
....\subject0205.noglasses,43681,1997-04-07
....\subject0206.normal,43681,1997-04-07
....\subject0207.rightlight,62924,1997-04-07
....\subject0208.sad,43439,1997-04-07
....\subject0209.sleepy,43518,1997-04-07
....\subject0210.surprised,45447,1997-04-07
....\subject0211.wink,43080,1997-04-07
....\subject0301.centerlight,26833,1997-04-07
....\subject0302.glasses,27292,1997-04-07
....\subject0303.happy,28032,1997-04-07
....\subject0304.leftlight,34932,1997-04-07
....\subject0305.noglasses,27895,1997-04-07
....\subject0306.normal,27895,1997-04-07
....\subject0307.rightlight,58502,1997-04-07
....\subject0308.sad,28266,1997-04-07
....\subject0309.sleepy,27462,1997-04-07
....\subject0310.surprised,28244,1997-04-07
....\subject0311.wink,27580,1997-04-07
....\subject0401.centerlight,28280,1997-04-07
....\subject0402.glasses,38740,1997-04-07
....\subject0403.happy,37556,1997-04-07
....\subject0404.leftlight,41187,1997-04-07
....\subject0405.noglasses,37351,1997-04-07
....\subject0406.normal,37126,1997-04-07
....\subject0407.rightlight,67257,1997-04-07
....\subject0408.sad,37126,1997-06-17
....\subject0409.sleepy,37972,1997-04-07
....\subject0410.surprised,37972,1997-04-07
....\subject0411.wink,37002,1997-04-07
....\subject0501.centerlight,30465,1997-04-07
....\subject0502.glasses,35701,1997-04-07
....\subject0503.happy,36056,1997-04-07
....\subject0504.leftlight,40304,1997-04-07
....\subject0505.noglasses,35649,1997-04-07
....\subject0506.normal,35427,1997-04-07
....\subject0507.rightlight,69306,1997-04-07
....\subject0508.sad,35278,1997-04-07
....\subject0509.sleepy,35436,1997-04-07
....\subject0510.surprised,35876,1997-04-07
....\subject0511.wink,36228,1997-04-07
....\subject0601.centerlight,35518,1997-04-07

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

  • nine
    Card game Nine, developing attention.
    2014-08-22 18:49:00下载
    积分:1
  • new3
    逆变电路 使用matlab自带的pwm发生器 离散仿真(unicerse matlab simulink pwm generator)
    2013-05-17 18:16:00下载
    积分:1
  • 9927416SHA_MatLab
    hi its useful for matlab developers
    2009-09-04 21:47:27下载
    积分:1
  • lab4_final
    analize of vocal signal in matlab
    2011-05-27 20:22:04下载
    积分:1
  • Hybrid_Adaptive_Control
    hybrid adaptive control
    2014-08-25 23:30:19下载
    积分:1
  • 蒙特卡洛仿真-n维球体积
    说明:  使用蒙特卡罗方法计算n维球体的体积,很有趣的程序(An interesting program for calculating the volume of n-dimensional spheres by Monte Carlo method)
    2021-03-31 18:19:09下载
    积分:1
  • Reversible_Jump_MCMC_Bayesian_Model_Selection
    This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )
    2008-03-07 23:23:12下载
    积分:1
  • 3924a8f7-4982-4925-887e-0285120b7b51
    说明:  这是MATLAB常用命令全集,有很详细的语法格式和实例(This is commonly used MATLAB command Works, had a very detailed syntax and examples)
    2008-10-23 12:55:58下载
    积分:1
  • image
    image enhancement in time domain and frequency domain
    2014-09-29 22:05:19下载
    积分:1
  • heartbeat
    heart beat monitor program for mikroC
    2015-04-07 14:18:08下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载