svm的使用
其中参数：-s SVC是SVM分类器，SVR是SVM回归
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| 0 -- C-SVC (multi-class classification)
1 -- nu-SVC (multi-class classification)
2 -- one-class SVM
3 -- epsilon-SVR (regression)
4 -- nu-SVR (regression)
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-t 核函数的类型: kernel_type : set type of kernel function (default 2)
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| 0 -- linear: u'*v
1 -- polynomial: (gamma*u'*v + coef0)^degree
2 -- radial basis function: exp(-gamma*|u-v|^2)
3 -- sigmoid: tanh(gamma*u'*v + coef0)
4 -- precomputed kernel (kernel values in training_set_file)
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分类,Classify a binary data with polynomial kernel (u’v+1)^3 and C = 10
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| svmtrain -s 0 -c 10 -t 1 -g 1 -r 1 -d 3
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回归
Introduction
LIBLINEAR is a linear classifier for data with millions of instances and features. It supports
- L2-regularized classifiers
- L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
- L1-regularized classifiers (after version 1.4)
- L2-loss linear SVM and logistic regression (LR)
- L2-regularized support vector regression (after version 1.9)
- L2-loss linear SVR and L1-loss linear SVR.
Main features of LIBLINEAR include
- Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
- Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
- Cross validation for model evaulation
- Automatic parameter selection
- Probability estimates (logistic regression only)
- Weights for unbalanced data
- MATLAB/Octave, Java, Python, Ruby interfaces
Multiclass classification with libSvm
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| unzip libsvm.zip
cd libsvm-3.21/
wget "http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/iris.scale"
more iris.scale
./svm-train svm-train iris.scale
./svm-train iris.scale
./svm-predict iris.scale iris.scale.model iris.scale.output
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预测
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| [root@bogon libsvm-3.21]# ./svm-predict iris.scale iris.scale.model iris.scale.output
Accuracy = 97.3333% (146/150) (classification)
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iris的数据格式,包含3个类的数据
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| 1 1:-0.722222 2:-0.166667 3:-0.864407 4:-0.833333
1 1:-0.555556 2:0.5 3:-0.79661 4:-0.916667
1 1:-0.833333 3:-0.864407 4:-0.916667
1 1:-0.444444 2:0.416667 3:-0.830508 4:-0.916667
1 1:-0.611111 2:0.0833333 3:-0.864407 4:-0.916667
2 1:0.5 3:0.254237 4:0.0833333
2 1:0.166667 3:0.186441 4:0.166667
2 1:0.444444 2:-0.0833334 3:0.322034 4:0.166667
2 1:-0.333333 2:-0.75 3:0.0169491 4:-4.03573e-08
2 1:0.222222 2:-0.333333 3:0.220339 4:0.166667
2 1:-0.222222 2:-0.333333 3:0.186441 4:-4.03573e-08
2 1:0.111111 2:0.0833333 3:0.254237 4:0.25
2 1:-0.666667 2:-0.666667 3:-0.220339 4:-0.25
2 1:0.277778 2:-0.25 3:0.220339 4:-4.03573e-08
2 1:-0.5 2:-0.416667 3:-0.0169491 4:0.0833333
2 1:-0.611111 2:-1 3:-0.152542 4:-0.25
2 1:-0.111111 2:-0.166667 3:0.0847457 4:0.166667
2 1:-0.0555556 2:-0.833333 3:0.0169491 4:-0.25
2 1:-1.32455e-07 2:-0.25 3:0.254237 4:0.0833333
2 1:-0.277778 2:-0.25 3:-0.118644 4:-4.03573e-08
2 1:0.333333 2:-0.0833334 3:0.152542 4:0.0833333
2 1:-0.277778 2:-0.166667 3:0.186441 4:0.166667
2 1:-0.166667 2:-0.416667 3:0.0508474 4:-0.25
2 1:0.0555554 2:-0.833333 3:0.186441 4:0.166667
2 1:-0.277778 2:-0.583333 3:-0.0169491 4:-0.166667
2 1:-0.111111 3:0.288136 4:0.416667
2 1:-1.32455e-07 2:-0.333333 3:0.0169491 4:-4.03573e-08
2 1:0.111111 2:-0.583333 3:0.322034 4:0.166667
2 1:-1.32455e-07 2:-0.333333 3:0.254237 4:-0.0833333
2 1:0.166667 2:-0.25 3:0.118644 4:-4.03573e-08
2 1:0.277778 2:-0.166667 3:0.152542 4:0.0833333
2 1:0.388889 2:-0.333333 3:0.288136 4:0.0833333
2 1:0.333333 2:-0.166667 3:0.355932 4:0.333333
2 1:-0.0555556 2:-0.25 3:0.186441 4:0.166667
2 1:-0.222222 2:-0.5 3:-0.152542 4:-0.25
2 1:-0.333333 2:-0.666667 3:-0.0508475 4:-0.166667
2 1:-0.333333 2:-0.666667 3:-0.0847458 4:-0.25
2 1:-0.166667 2:-0.416667 3:-0.0169491 4:-0.0833333
2 1:-0.0555556 2:-0.416667 3:0.38983 4:0.25
2 1:-0.388889 2:-0.166667 3:0.186441 4:0.166667
2 1:-0.0555556 2:0.166667 3:0.186441 4:0.25
2 1:0.333333 2:-0.0833334 3:0.254237 4:0.166667
2 1:0.111111 2:-0.75 3:0.152542 4:-4.03573e-08
2 1:-0.277778 2:-0.166667 3:0.0508474 4:-4.03573e-08
2 1:-0.333333 2:-0.583333 3:0.0169491 4:-4.03573e-08
2 1:-0.333333 2:-0.5 3:0.152542 4:-0.0833333
2 1:-1.32455e-07 2:-0.166667 3:0.220339 4:0.0833333
2 1:-0.166667 2:-0.5 3:0.0169491 4:-0.0833333
2 1:-0.611111 2:-0.75 3:-0.220339 4:-0.25
2 1:-0.277778 2:-0.416667 3:0.0847457 4:-4.03573e-08
2 1:-0.222222 2:-0.166667 3:0.0847457 4:-0.0833333
2 1:-0.222222 2:-0.25 3:0.0847457 4:-4.03573e-08
2 1:0.0555554 2:-0.25 3:0.118644 4:-4.03573e-08
2 1:-0.555556 2:-0.583333 3:-0.322034 4:-0.166667
2 1:-0.222222 2:-0.333333 3:0.0508474 4:-4.03573e-08
3 1:0.111111 2:0.0833333 3:0.694915 4:1
3 1:-0.166667 2:-0.416667 3:0.38983 4:0.5
3 1:0.555555 2:-0.166667 3:0.661017 4:0.666667
3 1:0.111111 2:-0.25 3:0.559322 4:0.416667
3 1:0.222222 2:-0.166667 3:0.627119 4:0.75
3 1:0.833333 2:-0.166667 3:0.898305 4:0.666667
3 1:-0.666667 2:-0.583333 3:0.186441 4:0.333333
3 1:0.666667 2:-0.25 3:0.79661 4:0.416667
3 1:0.333333 2:-0.583333 3:0.627119 4:0.416667
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预测结果,more iris.scale.output
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