登录
首页 » matlab » libsvm

libsvm

于 2014-10-27 发布 文件大小:1301KB
0 186
下载积分: 1 下载次数: 118
我要下载

代码说明：

  这个是一个SVM分类器，可以再行人检测时用来分类训练样本，再MATLAB中直接调用(This is an SVM classifier, when pedestrian detection can be used to classify the training sample, and then directly call MATLAB)

文件列表：

libsvm
......\libsvm3.1
......\.........\libsvm-3.1-[FarutoUltimate3.1Mcode]
......\.........\...................................\htm" target=_blank>COPYRIGHT,1497,2011-03-26
......\.........\...................................\FAQ.html,68901,2011-03-26
......\.........\...................................\htm" target=_blank>heart_scale,27670,2003-07-12
......\.........\...................................\java
......\.........\...................................\....\libsvm
......\.........\...................................\....\......\svm.java,61931,2011-03-26
......\.........\...................................\....\......\svm.m4,61280,2010-11-05
......\.........\...................................\....\......\svm_model.java,734,2010-09-12
......\.........\...................................\....\......\svm_node.java,115,2003-10-11
......\.........\...................................\....\......\svm_parameter.java,1288,2006-03-03
......\.........\...................................\....\......\svm_print_interface.java,87,2009-02-18
......\.........\...................................\....\......\svm_problem.java,136,2003-10-11
......\.........\...................................\....\libsvm.jar,49854,2011-03-26
......\.........\...................................\....\Makefile,624,2009-02-18
......\.........\...................................\....\svm_predict.java,4267,2009-03-18
......\.........\...................................\....\svm_scale.java,8944,2009-02-20
......\.........\...................................\....\svm_toy.java,11483,2010-12-13
......\.........\...................................\....\svm_train.java,8268,2010-01-27
......\.........\...................................\....\test_applet.html,81,2003-07-12
......\.........\...................................\Makefile,528,2010-09-12
......\.........\...................................\Makefile.win,1087,2010-09-12
......\.........\...................................\matlab
......\.........\...................................\......\heart_scale.mat,28904,2005-03-22
......\.........\...................................\......\libsvmread.c,3988,2011-02-24
......\.........\...................................\......\libsvmread.mexw32,20480,2011-06-08
......\.........\...................................\......\libsvmwrite.c,2123,2011-02-24
......\.........\...................................\......\libsvmwrite.mexw32,20480,2011-06-08
......\.........\...................................\......\make.m,396,2011-02-24
......\.........\...................................\......\Makefile,1481,2011-02-24
......\.........\...................................\......\htm" target=_blank>README,9289,2011-03-16
......\.........\...................................\......\svm.obj,65521,2011-06-08
......\.........\...................................\......\svmpredict.c,9063,2011-03-08
......\.........\...................................\......\svmpredict.mexw32,24576,2011-06-08
......\.........\...................................\......\svmtrain.c,11355,2011-06-08
......\.........\...................................\......\svmtrain.c.bak,11343,2011-03-08
......\.........\...................................\......\svmtrain.mexw32,45056,2011-06-08
......\.........\...................................\......\svm_model_matlab.c,7694,2011-02-24
......\.........\...................................\......\svm_model_matlab.h,201,2011-02-24
......\.........\...................................\......\svm_model_matlab.obj,6287,2011-06-08
......\.........\...................................\matlab-implement[by faruto]
......\.........\...................................\...........................\a_template_flow_usingSVM_class.m,2519,2011-06-08
......\.........\...................................\...........................\a_template_flow_usingSVM_regress.m,2338,2011-06-08
......\.........\...................................\...........................\ClassResult.m,2086,2011-07-07
......\.........\...................................\...........................\ClassResult_test.m,366,2011-07-07
......\.........\...................................\...........................\gaSVMcgForClass.m,3579,2011-07-16
......\.........\...................................\...........................\gaSVMcgForRegress.m,3463,2011-06-08
......\.........\...................................\...........................\gaSVMcgpForRegress.m,3744,2011-06-08
......\.........\...................................\...........................\libsvm参数说明.txt,2865,2011-06-22
......\.........\...................................\...........................\myprivate
......\.........\...................................\...........................\.........\gatbx[Sheffield]
......\.........\...................................\...........................\.........\................\bs2rv.m,3217,1998-04-22
......\.........\...................................\...........................\.........\................\contents.m,1835,1998-04-22
......\.........\...................................\...........................\.........\................\crtbase.m,1168,1998-04-22
......\.........\...................................\...........................\.........\................\crtbp.m,2187,1998-04-22
......\.........\...................................\...........................\.........\................\crtrp.m,2091,1998-04-22
......\.........\...................................\...........................\.........\................\migrate.m,7205,1998-04-22
......\.........\...................................\...........................\.........\................\mpga.m,4019,1998-04-22
......\.........\...................................\...........................\.........\................\mut.m,1609,1998-04-22
......\.........\...................................\...........................\.........\................\mutate.m,3437,1998-04-22
......\.........\...................................\...........................\.........\................\mutbga.m,4943,1998-04-22
......\.........\...................................\...........................\.........\................\mytest
......\.........\...................................\...........................\.........\................\......\gaSVM.m,2792,2009-12-23
......\.........\...................................\...........................\.........\................\ranking.m,4709,1998-04-22
......\.........\...................................\...........................\.........\................\recdis.m,1825,1998-04-22
......\.........\...................................\...........................\.........\................\recint.m,1895,1998-04-22
......\.........\...................................\...........................\.........\................\reclin.m,1953,1998-04-22
......\.........\...................................\...........................\.........\................\recmut.m,4852,1998-04-22
......\.........\...................................\...........................\.........\................\recombin.m,2438,1998-04-22
......\.........\...................................\...........................\.........\................\reins.m,5574,1998-04-22
......\.........\...................................\...........................\.........\................\rep.m,1208,1998-04-22
......\.........\...................................\...........................\.........\................\resplot.m,2080,1998-04-22
......\.........\...................................\...........................\.........\................\rws.m,1090,1998-04-22
......\.........\...................................\...........................\.........\................\scaling.m,1270,1998-04-22
......\.........\...................................\...........................\.........\................\select.m,2401,1998-04-22
......\.........\...................................\...........................\.........\................\sus.m,1319,1998-04-22
......\.........\...................................\...........................\.........\................\xovdp.m,1042,1998-04-22
......\.........\...................................\...........................\.........\................\xovdprs.m,1090,1998-04-22
......\.........\...................................\...........................\.........\................\xovmp.m,2795,1998-04-22
......\.........\...................................\...........................\.........\................\xovsh.m,1032,1998-04-22
......\.........\...................................\...........................\.........\................\xovshrs.m,1080,1998-04-22
......\.........\...................................\...........................\.........\................\xovsp.m,1043,1998-04-22
......\.........\...................................\...........................\.........\................\xovsprs.m,1090,1998-04-22
......\.........\...................................\...........................\.........\plotroc2009b.m,5374,2011-06-21
......\.........\...................................\...........................\pcaForSVM.m,1272,2011-06-08
......\.........\...................................\...........................\plotSVMroc.m,1304,2011-06-21
......\.........\...................................\...........................\plotSVMroc_test.m,381,2011-06-21
......\.........\...................................\...........................\plotSVMroc_test2.m,1002,2011-06-21
......\.........\...................................\...........................\psoSVMcgForClass.m,5586,2011-06-08
......\.........\...................................\...........................\psoSVMcgForRegress.m,5349,2011-06-08
......\.........\...................................\...........................\psoSVMcgpForRegress.m,6208,2011-06-08
......\.........\...................................\...........................\Readme[by faruto]CN.txt,1602,2009-11-21
......\.........\...................................\...........................\Readme[by faruto]EN.txt,2706,2011-06-09
......\.........\...................................\...........................\scaleForSVM.m,998,2011-06-08
......\.........\...................................\...........................\SVC.m,4139,2011-06-08
......\.........\...................................\...........................\SVC_test.m,1026,2011-06-08
......\.........\...................................\...........................\SVMcgForClass.m,2643,2011-06-08
......\.........\...................................\...........................\SVMcgForRegress.m,2546,2011-06-08

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

  • tsaiandzhangzhengyou
    本文件包括TSAI的摄像机标定法，及张正友的摄像机标定法，并给出标定模板，及标定数据，可以进行摄像机标定。(This document contains the camera calibration method TSAI, and Zhang are the Friends of the camera calibration method, and give calibration template, and calibration data, camera calibration can be carried out.)
    2009-03-29 10:33:35下载
    积分：1
  • knn1
    K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)
    2017-08-09 21:06:38下载
    积分：1
  • 基于均匀线阵的MUSIC算法的DOA估计
    说明：  基于天线阵列协方差矩阵的特征分解类DOA估计算法中，多重信号分类（MUSIC）算法具有普遍适用性，只要已知天线阵的分布形式，无论直线阵还是圆阵，不管阵元是否等间隔分布，都可以得到高分辨率的估计结果。(In the DOA estimation algorithm based on eigendecomposition of antenna array covariance matrix, the multi signal classification (Music) algorithm has universal applicability. As long as the distribution form of antenna array is known, whether the linear array or the circular array, whether the array elements are equally spaced or not, the high-resolution estimation results can be obtained.)
    2020-06-11 18:00:23下载
    积分：1
  • mypq
    基于Matlab的pq分解法的潮流计算，对在给定的节点中电力系统进行潮流分析(Calculated based on the trend of Matlab pq decomposition method for a given node in the power system load flow analysis)
    2014-11-19 20:35:49下载
    积分：1
  • book
    是我见过讲的最详细的关于稀疏信号的书，讲解了稀疏分解，以及书后的程序都是很好用的(Ive seen talking about the most detailed book about the sparse signal, explained the sparse decomposition, and program books are well used after)
    2013-12-05 21:14:04下载
    积分：1
  • Dfrft
    这是一个经典的分数阶傅里叶变换的matlabs源代码，如果想学习分数阶傅里叶变换的话，这是一个很好的学习程序(if you wang to learn the fractionan fourier transform ,then this code is a perfact matlab code )
    2012-08-31 17:03:28下载
    积分：1
  • eof
    Matlab编写的EOF分析的代码，气象水文海洋专用(Prepared by the EOF analysis of Matlab code, exclusive of marine meteorological and hydrological)
    2010-03-15 13:53:40下载
    积分：1
  • 802p11TGn_ChannelModel
    matlab编写的802.11n的信道模型仿真(matlab prepared 802.11n channel model simulation)
    2021-04-06 20:29:02下载
    积分：1
  • 采用小波进行图像消噪
    数字图像在产生过程中经常会受到噪音污染。因此，对于数字图像应该对其进行去噪音处理。在传统的基于傅里叶变换的信号去噪音方法中，可以使得信号和噪音的频带重叠部分尽可能的小，这样就可以在频域通过时，不需改变滤波方法而将信号和噪音分隔开来。但是，如果信号和噪音的频域重合时，用该方法进行噪音处理的效果就比较差。(digital image in the selection process will be subject to regular noise pollution. Therefore, the digital image should be carried out to deal with the noise. Based on the traditional Fourier Transform method of signal to noise, can result in a signal and noise bands overlap small as possible, so it can be adopted in the frequency domain, the need to change methods of filtering the signal and noise to separate. However, if the signal and noise in the frequency domain coincidence, the method for dealing with the effects of noise on the relatively poor.)
    2005-07-12 11:28:35下载
    积分：1
  • Prj1_exp1
    求解的长直接地金属槽和平板电容中的电场，并画出等位线分布图(Calculation of Electron Physics )
    2020-10-15 21:57:30下载
    积分：1
  • 696518资源总数
  • 105958会员总数
  • 18今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2025 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！