I'd like to implement an ensemble of linear SVM classifiers using a custom implementation of Adaboost.
Is it possible to add weights to certain samples in the training set before letting a linear SVM train on it?
Note: I'm not referring to class weights to correct imbalanced TP/TN training data but set the importance of individual samples as done by the Adaboost algorithm.
If it's not possible to do so, how can I simulate giving more importance to certain samples? Will it be enough to add the sample x number of times in the training set? Do I add the weights of the sample as an extra feature?
Thanks in advance