Abstract: Many practical problems have involved classification technology which can effectively reduce the comprehension difference between users and computers.In the traditional multiclass Boosting methods,multiclass loss function does not necessarily have guess-aversion,and the combinations of multiclass weak learners are confined to linear weighted sum.To get a final classifier with high accuracy,multiclass loss function need contain three main properties,namely margin maximization,Bayes consistency and guess-aversion.Moreover,the weakness of weak learners may limit the performance of linear classifier,but their nonlinear combinations should provide strong discrimination.Therefore,we designed an adaptive multiclass Boosting classifier,namely SOHPBoost algorithm.At every iteration,our algorithm can add the best weak learner to the current ensemble according to vector addition or Hadamard product.This adaptive process can create the sum of Hadamard products of weak learners,and mine the hidden structure of dataset.The experi-ments show that SOHPBoost algorithm can provide more advantageous performance of multiclass classification.

Key words: Multiclass Boosting,Loss function,Guess-aversion,Nonlinear combination