Question

我试图在MNIST数据集上训练OpenCV SVM，我得到了奇怪的结果，即测试准确率为10％。知道出了什么问题吗？提前谢谢。

以下是我使用的参数：

Ptr<SVM> model = SVM::create();
         model->setType(SVM::C_SVC);
         model->setKernel(SVM::RBF);
         model->setC(10);
         model->setGamma(0.01);

训练：

model->train(tdata);

测试:(来自letter_recog.cpp样本）

 static void test_and_save_classifier(const Ptr<StatModel>& model,
                                 const Mat& data, const Mat& responses,
                                 int ntrain_samples, int rdelta,
                                 const string& filename_to_save)
{
int i, nsamples_all = data.rows;
double train_hr = 0, test_hr = 0;

// compute prediction error on 
// train data[0 , ..., ntrain_samples-1]; and 
// test data[0 , ..., nsamples_all-1]


before = static_cast<double>(getTickCount());

for( i = 0; i < nsamples_all; i++ )
{
    Mat sample = data.row(i);


    // The method is used to predict the response for a new sample. 
    // In case of a classification, the method returns the class label.        

    float r = model->predict( sample );  /// sample is the row feature vector


    // Tally correct classifications
    // +1 if prediction is correct
    // +0 if prediction is wrong 
    r = std::abs(r + rdelta - responses.at<int>(i)) <= FLT_EPSILON ? 1.f : 0.f;

    if( i < ntrain_samples )
        train_hr += r;
    else
        test_hr += r;
}

after = static_cast<double>(getTickCount());
duration_in_ms = 1000.0*(after - before)/getTickFrequency();
cout << "Prediction for all data completed after "<< duration_in_ms <<" ms...\n";
cout << "Average prediction time per sample is "<< duration_in_ms/nsamples_all <<" ms.\n";

test_hr /= nsamples_all - ntrain_samples;
train_hr = ntrain_samples > 0 ? train_hr/ntrain_samples : 1.;
/// Note: 0 training samples here will give 100 % training error


printf( "Recognition rate: train = %.2f%%, test = %.2f%%\n",
        train_hr*100., test_hr*100. );

if( !filename_to_save.empty() )
{
    model->save( filename_to_save );
}
}

试运行：

trainVecLabels.size() = 60000
trainVecLabels[0] = 5
testData.size() = 10000
testVecLabels.size() = 10000
testVecLabels[0] = 7
data.size() = [784 x 70000]
responses.size() = [1 x 70000]
Training the classifier ...
Training completed after 11.1005 mins...
Testing and saving ...
Prediction for all data completed after 443715 ms...
Average prediction time per sample is 6.33879 ms.
Recognition rate: train = 24.92%, test = 10.09%

填充物四处走动＆＃34;更多细节错误＆＃34;在StackOverflow中

Vapnik和Chervonenkis首先介绍了支持向量机。核心思想是找到最佳超平面来分离数据集，而理论上有无限超平面来分离数据集。选择超平面，以便到两者的最近数据点的距离课程最大化。跨越超平面的点是支持向量，因此称为支持向量机。

在MNIST上，OpenCV SVM奇怪的测试误差为10％

0 个答案: