文件名称:LGMMC_v2
- 所属分类：
- matlab例程
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 247kb
- 下载次数：
- 0次
- 提 供 者：
- litin*****
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
LGMMC是一个最大间隔聚类包。该计划包括了该算法的MATLAB代码的LG - MMC卡
-LGMMC is a package for maximum margin based clustering. The package includes the MATLAB code of the algorithm LG-MMC.
-LGMMC is a package for maximum margin based clustering. The package includes the MATLAB code of the algorithm LG-MMC.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
lgmmc(small data)\echocardiogram.mat
.................\experiments.m
.................\LG_MMC_v0.1\costsvmclass_lgmmc.m
.................\...........\gaussian_kernel.m
.................\...........\gradsvmclass_lgmmc.m
.................\...........\lgmmc.m
.................\...........\LGMMC_train.m
.................\...........\Max_Violated_y.m
.................\...........\mklsvmupdate_lgmmc.m
.................\...........\mklsvm_lgmmc.m
.................\...........\normalization.m
.................\...........\sumKbeta_lgmmc.m
.................\...........\svmclass_lgmmc.m
.................\LG_MMC_v0.1
.................\Readme.htm
lgmmc(small data)
......large scale data)\adult-8a.mat
.......................\experiments.m
.......................\LG-MMC C++\blas\blas.h
.......................\..........\....\blasp.h
.......................\..........\....\daxpy.c
.......................\..........\....\daxpy.mexw64.pdb
.......................\..........\....\daxpy.obj
.......................\..........\....\ddot.c
.......................\..........\....\ddot.obj
.......................\..........\....\dnrm2.c
.......................\..........\....\dnrm2.obj
.......................\..........\....\dscal.c
.......................\..........\....\dscal.obj
.......................\..........\....\Makefile
.......................\..........\blas
.......................\..........\linear.cpp
.......................\..........\linear.cpp.bak
.......................\..........\linear.h
.......................\..........\linear.h.bak
.......................\..........\matlab\linear_model_matlab.c
.......................\..........\......\linear_model_matlab.c.bak
.......................\..........\......\linear_model_matlab.h
.......................\..........\......\make.m
.......................\..........\......\predict.c
.......................\..........\......\predict.mexw32
.......................\..........\......\read_sparse.c
.......................\..........\......\read_sparse.mexw32
.......................\..........\......\train.c
.......................\..........\......\train.c.bak
.......................\..........\......\train.mexw32
.......................\..........\matlab
.......................\..........\predict.c
.......................\..........\train.c
.......................\..........\tron.cpp
.......................\..........\tron.h
.......................\LG-MMC C++
.......................\..MMC_large_scale_v0.1\lgmmc.m
.......................\......................\LGMMC_prediction_large_scale.m
.......................\......................\LGMMC_train_large_scale.m
.......................\......................\Max_Violated_y.m
.......................\......................\normalization.m
.......................\LGMMC_large_scale_v0.1
.......................\Readme.htm
lgmmc(large scale data)
.................\experiments.m
.................\LG_MMC_v0.1\costsvmclass_lgmmc.m
.................\...........\gaussian_kernel.m
.................\...........\gradsvmclass_lgmmc.m
.................\...........\lgmmc.m
.................\...........\LGMMC_train.m
.................\...........\Max_Violated_y.m
.................\...........\mklsvmupdate_lgmmc.m
.................\...........\mklsvm_lgmmc.m
.................\...........\normalization.m
.................\...........\sumKbeta_lgmmc.m
.................\...........\svmclass_lgmmc.m
.................\LG_MMC_v0.1
.................\Readme.htm
lgmmc(small data)
......large scale data)\adult-8a.mat
.......................\experiments.m
.......................\LG-MMC C++\blas\blas.h
.......................\..........\....\blasp.h
.......................\..........\....\daxpy.c
.......................\..........\....\daxpy.mexw64.pdb
.......................\..........\....\daxpy.obj
.......................\..........\....\ddot.c
.......................\..........\....\ddot.obj
.......................\..........\....\dnrm2.c
.......................\..........\....\dnrm2.obj
.......................\..........\....\dscal.c
.......................\..........\....\dscal.obj
.......................\..........\....\Makefile
.......................\..........\blas
.......................\..........\linear.cpp
.......................\..........\linear.cpp.bak
.......................\..........\linear.h
.......................\..........\linear.h.bak
.......................\..........\matlab\linear_model_matlab.c
.......................\..........\......\linear_model_matlab.c.bak
.......................\..........\......\linear_model_matlab.h
.......................\..........\......\make.m
.......................\..........\......\predict.c
.......................\..........\......\predict.mexw32
.......................\..........\......\read_sparse.c
.......................\..........\......\read_sparse.mexw32
.......................\..........\......\train.c
.......................\..........\......\train.c.bak
.......................\..........\......\train.mexw32
.......................\..........\matlab
.......................\..........\predict.c
.......................\..........\train.c
.......................\..........\tron.cpp
.......................\..........\tron.h
.......................\LG-MMC C++
.......................\..MMC_large_scale_v0.1\lgmmc.m
.......................\......................\LGMMC_prediction_large_scale.m
.......................\......................\LGMMC_train_large_scale.m
.......................\......................\Max_Violated_y.m
.......................\......................\normalization.m
.......................\LGMMC_large_scale_v0.1
.......................\Readme.htm
lgmmc(large scale data)