The task is classify authentication of banknotes using active learning and passive learning approaches.
The Banknote Authentication dataset contains data extracted from images that were taken for the evaluation of an authentication procedure for banknotes. It has 1372 instances and 5 real-valued attributes (variance of Wavelet Transformed image, skewness of Wavelet Transformed image, curtosis of Wavelet Transformed image, entropy of image and class).
A Support Vector Machine (SVM) has been trained with a pool of 10 randomly selected data points from the training set using linear kernel and L1 penalty. The penalty parameter is selected using 10-fold cross-validation. This process is repeated by adding 10 other randomly selected data points to the pool, until all the 900 training points have been used. The test error for all 90 SVMs that were trained using 10, 20, 30, ... 900 data points are calculated. This process is repeated 50 times (monte-carlo simulation) and test errors are plotted for each simulation.
A Support Vector Machine (SVM) has been trained with a pool of 10 randomly selected data points from the training set using linear kernel and L1 penalty. The penalty parameter is selected using 10-fold cross-validation. Then, 10 training data points closest to the hyperplane of the SVM are chosen and added to the pool. This process is repeated until all the 900 training data points have been used. The test error for all 90 SVMs that were trained using 10, 20, 30, ... 900 data points are calculated. This process is repeated 50 times (monte-carlo simulation) and test errors are plotted for each simulation.