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matlab实现SVM算法进行分类

matlab实现SVM算法进⾏分类

1.实验内容

（1）掌握⽀持向量机（SVM）的原理，核函数类型选择以及核参数选择原则等，并⽤malab的SVM函数求解各类分类问题实例。

（2）熟悉基于libsvm⼆分类的⼀般流程与⽅法，并对"bedroom,forest"两组数据进⾏分类（⼆分类），最后对试验分类的准确率进⾏分析。

2.实验过程

（1）线性分类

>> sp = [3,7;6,4;4,6;7.5,6.5]

sp =

3.0000 7.0000

6.0000 4.0000

4.0000 6.0000

7.5000 6.5000

>> nsp = size(sp)

nsp =

4 2

>> sn = [1,3;5,2;7,3;3,4;6,1]

sn =

1 3

5 2

7 3

3 4

6 1

>> nsn = size(sn)

nsn =

5 2

>> sd = [sp;sn]

sd =

3.0000 7.0000

6.0000 4.0000

4.0000 6.0000

7.5000 6.5000

1.0000 3.0000

5.0000 2.0000

7.0000 3.0000

3.0000

4.0000

6.0000 1.0000

>> lsd = [true true true true false false false false false]

lsd =

1×9 logical 数组

1 1 1 1 0 0 0 0 0

>> Y = nominal(lsd)

Y =

1×9 nominal 数组

true true true true false false false false false

>> figure(1);

>> subplot(1,2,1)

>> plot(sp(1:nsp,1),sp(1:nsp,2),'m+');

>> hold on

>> plot(sn(1:nsn,1),sn(1:nsn,2),'c*');

>> subplot(1,2,2)

>> svmStruct = svmtrain(sd,Y,'showplot',true);

警告: svmtrain will be removed in a future release. Use fitcsvm instead. > In svmtrain (line 230)

线性分类结果：

（2）⾮线性分类

>> sp = [3,7;6,6;4,6;5,6.5]

可利霉素说明书

sp =

3.0000 7.0000

6.0000 6.0000

4.0000 6.0000

5.0000

6.5000

>> nsp = size(sp);

>> sn = [1,2;3,5;7,3;3,4;6,2.7;4,3;2,7];

>> nsn = size(sn)

nsn =

7 2

sd =

3.0000 7.0000

6.0000 6.0000

4.0000 6.0000

5.0000

6.5000

1.0000

2.0000

3.0000 5.0000

7.0000 3.0000

3.0000

4.0000

6.0000 2.7000

4.0000 3.0000

2.0000 7.0000

>> lsd = [true true true true false false false false false false false]

lsd =

1×11 logical 数组

1 1 1 1 0 0 0 0 0 0 0

>> Y = nominal(lsd)

Y =

1×11 nominal 数组

true true true true false false false false false false false

>> figure(1);

>> subplot(1,2,1)

>> plot(sp(1:nsp,1),sp(1:nsp,2),'m+');

>> hold on

>> plot(sn(1:nsn,1),sn(1:nsn,2),'c*');

>> subplot(1,2,2)

>> svmStruct = svmtrain(sd,Y,'kernel_Function','quadratic','showplot',true);

警告: svmtrain will be removed in a future release. Use fitcsvm instead.

> In svmtrain (line 230)

>> %use the trained svm (svmStruct) to classify the data

>> RD = svmclassify(svmStruct,sd,'showplot',true)

警告: svmclassify will be removed in a future release. Use the predict method of an object returned by fitcsvm instead. > In svmclassify (line 47)

RD =

11×1 nominal 数组

true

false

>> % RD is the classification result vector

（3）⾼斯分类

>> sp = [5,4.5;3,7;6,6;4,6;5,6.5]%positive sample points sp =

5.0000 4.5000

3.0000 7.0000

6.0000 6.0000

4.0000 6.0000

5.0000

6.5000

>> nsp = size(sp);

>> sn = [1,2;3,5;7,3;3,4;6,2.7;4,3;2,7]

sn =

固定床反应器

1.0000

2.0000

3.0000 5.0000

7.0000 3.0000

3.0000

4.0000

6.0000 2.7000

4.0000 3.0000

2.0000 7.0000

>> nsn = size(sn)

nsn =

7 2

>> sd = [sp,sn]

错误使⽤ horzcat

串联的矩阵的维度不⼀致。

>> sd = [sp;sn]

柴油机起动器sd =

5.0000 4.5000

3.0000 7.0000

6.0000 6.0000

4.0000 6.0000

5.0000

6.5000

1.0000

2.0000

3.0000 5.0000

7.0000 3.0000

3.0000

4.0000

6.0000 2.7000

4.0000 3.0000

热流道温控器2.0000 7.0000

>> lsd = [true true true true true false false false false false false false]

lsd =

1×12 logical 数组张力计算

1 1 1 1 1 0 0 0 0 0 0 0

>> Y = nominal(lsd)

Y =

1×12 nominal 数组

变速箱试验台true true true true true false false false false false false false

>> figure(1);

>> subplot(1,2,1)

>> plot(sp(1:nsp,1),sp(1:nsp,2),'m+');

>> hold on

>> plot(sn(1:nsn,1),sn(1:nsn,2),'c*');

>> subplot(1,2,2)

>> svmStruct = svmtrain(sd,Y,'Kernel_Function','rbf','rbf_sigma',0.6,'method','SMO','showplot',true);

警告: svmtrain will be removed in a future release. Use fitcsvm instead.

> In svmtrain (line 230)

>> % use the trained svm (svmStruct) to classify the data

>> RD = svmclassify(svmStruct,sd,'showplot',true)

警告: svmclassify will be removed in a future release. Use the predict method

of an object returned by fitcsvm instead.

> In svmclassify (line 47)

RD =

12×1 nominal 数组

true

false

>> %RD is the classification result vector

⾼斯分类结果图

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