Similar to Multi-view Majority Voting Ad-aBoost, at each round t of Multi-view Weighted Voting AdaBoost, we separately train a set of single view classifiers {h t v } over the training instances with a weighted distribution D t , one for each language (view) v. But instead of taking a majority vote, we set the combination base classifier h t as a linear combination of the single view classifiers with a set of weight parameters where In order to obtain the weight parameters, we train this linear combination model on the weighted training set, using the outputs of the single-view classifiers as features.
we minimize the following weighted least square loss on the training instances to to obtain the β values: With the obtained base classifier h t in Eq.