登录
首页 » matlab » SVM-Classification

SVM-Classification

于 2021-04-07 发布 文件大小:3886KB
0 194
下载积分: 1 下载次数: 724

代码说明:

  Matlab写的SVM图像分类程序,是完整的可运行的图像分类程序,高手可以研究一下。(Matlab write SVM image classification procedure is complete working image classification procedures, experts can look at.)

文件列表:

SVM分类程序
...........\AFEm.m,3458,2013-06-06
...........\bay_errorbar.m,5785,2013-06-06
...........\bay_initlssvm.m,2003,2013-06-06
...........\bay_lssvm.m,10345,2013-06-06
...........\bay_lssvmARD.m,8187,2013-06-06
...........\bay_modoutClass.m,9358,2013-06-06
...........\bay_optimize.m,5977,2013-06-06
...........\bay_rr.m,4312,2013-06-06
...........\bootsp.m,1631,2013-06-06
...........\bootstrap.m,282,2013-06-06
...........\changelssvm.m,5576,2013-06-06
...........\cilssvm.m,4462,2013-06-06
...........\code.m,4245,2013-06-06
...........\codedist_bay.m,2118,2013-06-06
...........\codedist_hamming.m,756,2013-06-06
...........\codedist_loss.m,2018,2013-06-06
...........\codelssvm.m,4126,2013-06-06
...........\code_ECOC.m,5197,2013-06-06
...........\code_MOC.m,550,2013-06-06
...........\code_OneVsAll.m,364,2013-06-06
...........\code_OneVsOne.m,579,2013-06-06
...........\comp.m,359,2013-06-06
...........\crossvalidate.m,5350,2013-06-06
...........\crossvalidatelssvm.m,3773,2013-06-06
...........\csa.m,2901,2013-06-06
...........\data1.txt,5998384,2013-06-06
...........\democlass.m,3459,2013-06-06
...........\democonfint.m,2143,2013-06-06
...........\demofun.m,3972,2013-06-06
...........\demomodel.m,4772,2013-06-06
...........\demomulticlass.m,2299,2013-06-06
...........\demo_fixedclass.m,2251,2013-06-06
...........\demo_fixedsize.m,3233,2013-06-06
...........\demo_yinyang.m,3447,2013-06-06
...........\denoise_kpca.m,3598,2013-06-06
...........\eign.m,3787,2013-06-06
...........\gcrossvalidate.m,3302,2013-06-06
...........\gcrossvalidatelssvm.m,2022,2013-06-06
...........\gridsearch.m,6927,2013-06-06
...........\initlssvm.m,3327,2013-06-06
...........\kentropy.m,2206,2013-06-06
...........\kernel_matrix.m,2636,2013-06-06
...........\kernel_matrix2.m,477,2013-06-06
...........\kpca.m,6137,2013-06-06
...........\latentlssvm.m,2398,2013-06-06
...........\leaveoneout.m,3667,2013-06-06
...........\leaveoneoutlssvm.m,2334,2013-06-06
...........\linesearch.m,3758,2013-06-06
...........\linf.m,313,2013-06-06
...........\lin_kernel.m,531,2013-06-06
...........\lssvm.m,1762,2013-06-06
...........\lssvmMATLAB.m,2082,2013-06-06
...........\mean_acc.mat,183,2013-06-06
...........\mean_time.mat,187,2013-06-06
...........\MeasureError.asv,725,2013-06-06
...........\MeasureError.m,790,2013-06-06
...........\medae.m,311,2013-06-06
...........\misclass.m,693,2013-06-06
...........\MLP_kernel.m,608,2013-06-06
...........\mse.m,285,2013-06-06
...........\plotlssvm.m,9963,2013-06-06
...........\poly_kernel.m,623,2013-06-06
...........\postlssvm.m,4838,2013-06-06
...........\predict.m,3485,2013-06-06
...........\predlssvm.m,5031,2013-06-06
...........\preimage_rbf.m,4452,2013-06-06
...........\prelssvm.m,6288,2013-06-06
...........\progress.m,1247,2013-06-06
...........\range.m,173,2013-06-06
...........\RBF_kernel.m,1105,2013-06-06
...........\rcrossvalidate.m,5945,2013-06-06
...........\rcrossvalidatelssvm.m,4085,2013-06-06
...........\ridgeregress.m,1436,2013-06-06
...........\ripley.mat,4100,2013-06-06
...........\robustlssvm.m,3216,2013-06-06
...........\roc.m,7496,2013-06-06
...........\rsimplex.m,9888,2013-06-06
...........\sample.asv,100,2013-06-06
...........\sample.m,100,2013-06-06
...........\simann.m,5845,2013-06-06
...........\simlssvm.m,6421,2013-06-06
...........\simplex.m,9778,2013-06-06
...........\smootherlssvm.m,1033,2013-06-06
...........\tbform.m,1936,2013-06-06
...........\test.data,2029542,2013-06-06
...........\test1.asv,38,2013-06-06
...........\test1.m,33,2013-06-06
...........\test2.asv,520,2013-06-06
...........\test2.m,522,2013-06-06
...........\test3.asv,3888,2013-06-06
...........\test3.m,4117,2013-06-06
...........\train.data,14512253,2013-06-06
...........\trainlssvm.m,8732,2013-06-06
...........\tri.asv,5120,2013-06-06
...........\tri.m,5316,2013-06-06
...........\trimmedmse.m,1711,2013-06-06
...........\tunelssvm.asv,22592,2013-06-06
...........\tunelssvm.m,22592,2013-06-06
...........\usps_t1.m,968,2013-06-06

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

  • Draw-a-rectangle-matlab
    本程序在图像上指定位置话红色的矩形框作为标记,用在目标跟踪上很有用。(This procedure is specified, then the position of the red rectangle on an image as a mark for use in target tracking useful.)
    2014-08-05 20:59:24下载
    积分:1
  • spherical-coordinates
    可以实现平面图像到球面坐标的映射变换,效果较好,附原始图片。(Plane image mapping transformation to spherical coordinates better with the original picture.)
    2012-12-11 13:42:16下载
    积分:1
  • zhixinfa
    灰度质心法提取图像圆点中心坐标程序源代码(Extracted gray-scale image centroid coordinates of the dot center of the source code)
    2011-05-12 19:13:11下载
    积分:1
  • digital_image
    数字图像处理中sobel算子的手写代码;canny手写代码;加噪声并滤波的调用库函数代码(Digital image using sobel & canny)
    2015-11-30 20:57:03下载
    积分:1
  • NLMD
    说明:  利用非局部均值的算法对图像做去噪,速度比较快,但效果一般,存在一定局限性(Using non-local mean algorithm to denoise image is faster, but the effect is general and there are some limitations.)
    2020-08-18 09:08:22下载
    积分:1
  • EBMA--cif
    通过使用块匹配法实现运动矢量的检测,块大小和搜索区域大小可自定(Achieved through the use of block matching motion vector detection, block size and search area size can be customized)
    2010-12-25 16:35:47下载
    积分:1
  • medcalinhance
    本程序实现医学图像的增强,实验显示,处理后的医学图像轮廓清晰,可识性较好。(This procedure to achieve medical image enhancement, experiments showed that the treated medical image outline a clear, identifiable better.)
    2021-01-26 23:18:41下载
    积分:1
  • hongwaileida
    针对粒子概率假设密度(PH D)滤波算法在虚警、漏检情况下,b标状态估计不稳定和b标可观测性车弱的问题,提出了一种基于序贯融合的粒子PH D滤波方法,利用雷达和红外传感器多目标进行融合跟踪杏基本思想是先对红外传感器进行粒子PH D滤波,丙将红外传感器滤波结果作为雷达的预测值,然后利用P达观测的数据进行更新,这样通过雷达和红外传感器交替上作保证目标状态的可观测性,从滤波器输出结身即可得到目标的状态信息仿真结果表明,在虚警、漏检和密集目标环境下,该方法是有效的和稳健的(The problem of target state estimation instability and observability weakerin the presence offalse alarms and missed detection was deal with. On the basis of sequential fusion, a particleprobabil- ity hypothesis density(PHD) filter for multrsensor multrtarget tracking was proposed. Observed da to collected from the infrared sensor was estimated with the particle PHD filter. Then the results from the filter were set as the radar predicted value by the radar observations. The multrtarget state can be updated to guarantee observing the target state. In this way, the global state is updated at the fusion center. Simulation results show that the proposed algorithm is effective and robust under the false warning, omission and concentrated target environment. )
    2012-03-06 16:27:41下载
    积分:1
  • diedafa
    图像分割迭代法,对于RGB图像灰度图像的分割有非常好的效果,对初学图像分割的有很大的帮助。(Image segmentation iterative method for gray image segmentation RGB images have a very good effect, image segmentation for beginners great help.)
    2014-09-16 10:21:35下载
    积分:1
  • 特征提取
    说明:  采用gabor算法,灰度共生矩阵、灰度梯度共生矩阵提取图像纹理信息(Using Gabor algorithm, gray level co-occurrence matrix and gray level gradient co-occurrence matrix to extract image texture information)
    2020-11-05 17:49:50下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载