where ` ( ( :(bi= wi { (xi yi pn (bi= wi pn can be interpreted as the weighted zero-one training loss at iteration for any the program ( is minimized by classifier that minimizes this weighted training lossthat iscb (xargmin ` ( ( cc substituting ( into ( and solving for the optimal gives ` ( (cb ab ln ` ( (cb ( this gives the adaboost algorithmsummarized below algorithm adaboost inputtraining set {(xi yi )}ni= and the number of boosting rounds outputadaboost prediction function set ( for to do ( / for to do fit classifier cb on the training set by solving cb argmin ` ( (cargmin cc cc set ab ln ` ( (cb ` ( (cb (bi= wi { (xi yi pn (bi= wi pn /update weights for to do ( + (bi exp{-yi ab cb (xi )pb return ( : = ab cb ( algorithm is quite intuitive at the first step ( )adaboost assigns an equal weight ( / to each training sample (xi yi in the set {(xi yi )}ni= note thatin this casethe weighted zero-one training loss is equal to the regular zero-one training loss at each successive step the weights of observations that were incorrectly classified by the previous boosting prediction function gb are increasedand the weights of correctly classified observations are decreased due to the use of the weighted zero-one lossthe set of incorrectly classified training samples will receive an extra weight and thus have better chance of being classified correctly by the next classifier cb+ as soon as the adaboost algorithm finds the prediction function gb the final classification is delivered via sign ab cb (xb=