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Readme for the S4VM Package
version May 9, 2011
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The package includes the MATLAB code of the semi-supervised algorithm S4VM, which towards making unlabeled data never hurt, or safe semi-supervised algorithm [1].
[1] Y.-F. Li, Z.-H. Zhou. Towards Making Unlabeled Data Never Hurt. In: Proceedings of the 28th International Conference on Machine Learning (ICML'11), Bellevue, Washington, 2011.
Note that the Matlab version of Libsvm [2] is needed (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) to implement supervised SVM with different weights of labeled and unlabeled instances.It should be included to facilitate the implementation of S4VM.
For your convenience, the matlab code of libsvm using different insatnce weights is included in the package.
[2] C.-C. Chang and C.-J. Lin. Libsvm: a library for support vector machines, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Technical Report, 2001.
You will find an example of using this code in the 'example.m' function. The example data is wdbc data. In particular, 10 examples are labeled and the rest are unlabeled.
In our ICML'11 experiment, all the features are nomalized to [0,1] in advanced.
ATTN:
- This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (zhouzh@nju.edu.cn).
- This package was developed by Mr. Teng Zhang (zhangt@lamda.nju.edu.cn) and Mr.Yu-Feng Li (liyf@lamda.nju.edu.cn). For any
problem concerning the code, please feel free to contact Mr. Li or Mr. Zhang.
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S4VM算法附Matlab代码.zip
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c:5个
m:5个
mexw32:4个
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1.版本:matlab2014/2019a/2021a,内含运行结果,不会运行可私信 2.附赠案例数据可直接运行matlab程序。 3.代码特点:参数化编程、参数可方便更改、代码编程思路清晰、注释明细。 4.适用对象:计算机,电子信息工程、数学等专业的大学生课程设计、期末大作业和毕业设计。 5.作者介绍:某大厂资深算法工程师,从事Matlab算法仿真工作10年;擅长智能优化算法、神经网络预测、信号处理、元胞自动机等多种领域的算法仿真实验,更多仿真源码、数据集定制私信+。
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S4VM算法附Matlab代码.zip (34个子文件)
S4VM算法附Matlab代码
S4VM
wdbc10.mat 59KB
S4VM.m 10KB
libsvm-mat-2.89-3-weight
libsvmwrite.mexw64 10KB
COPYRIGHT 1KB
libsvmread.mexw32 9KB
svm.obj 160KB
README 9KB
svm.cpp 66KB
Makefile 1KB
svmtrain.c 12KB
make.m 743B
svmpredict.c 9KB
svmpredict.mexw64 24KB
svm_model_matlab.h 201B
svm.cpp.bak 66KB
aa.mat 4KB
main.m 849B
svm_model_matlab.obj 9KB
svm.h 3KB
svmtrain.c.bak 12KB
libsvmwrite.mexw32 8KB
svm.h.bak 3KB
libsvmread.c 4KB
libsvmread.mexw64 11KB
svm_model_matlab.c.bak 8KB
libsvmwrite.c 2KB
heart_scale.mat 28KB
svmpredict.mexw32 21KB
svm_model_matlab.c 8KB
svmtrain.mexw32 53KB
svmtrain.mexw64 67KB
example.m 286B
readme.txt 2KB
example.m 409B
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