Matlab中的数字图像集上的SVM分类

问题描述

我必须在数字数据集上使用SVM分类器.数据集由数字28x28的图像和总共2000张图像组成.我尝试使用svmtrain，但是matlab给出了svmtrain已被删除的错误.所以现在我正在使用fitcsvm.

I have to use SVM classifier on digits dataset. The dataset consists of images of digits 28x28 and a toal of 2000 images.I tried to use svmtrain but the matlab gave an error that svmtrain has been removed. so now i am using fitcsvm.

我的代码如下:

labelData = zeros(2000,1);

for i=1:1000
labelData(i,1)=1;
end

for j=1001:2000
labelData(j,1)=1;
end

SVMStruct =fitcsvm(trainingData,labelData)
%where training data is the set of images of digits.

我需要知道如何使用svm预测测试数据的输出?另外我的代码正确吗?

I need to know how i can predict the outputs of test data using svm? Further is my code correct?

推荐答案

您要查找的功能是 predict .它以SVM对象作为输入，后跟一个数据矩阵，并返回预测的标签.确保不对所有数据而是对合理的子集(通常为70％)训练模型.您可以使用交叉验证的准备工作:

The function that you are looking for is predict. It takes the SVM-object as input followed by a data-matrix and returns the predicted labels.Make sure that you do not train your model on all data but on a reasonable subset (usually 70%). You can use the cross-validation preparation:

% create cross-validation object
cvp = cvpartition(Lbl,'HoldOut',0.3);
% extract logical vectors for training and testing data
lgTrn = cvp.training;
lgTst = cvp.test;

% train SVM
mdl = fitcsvm(Dat(lgTrn,:),Lbl(lgTrn));

% test / predict SVM
Lbl_prd = predict(mdl,Dat(lgTst,:));

请注意，您的标签会产生一个由1构成的向量.

Note that your labeling produces a single vector of ones.

The Mathworks将svmtrain更改为fitcsvm的原因很简洁.现在很明显是分类"(fit c svm)还是回归"(fit r svm).

The reason why The Mathworks changed svmtrain to fitcsvm is conciseness. It is now clear whether it is "classification" (fitcsvm) or "regression" (fitrsvm).

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