Finally, we update the weight vector W t trained with boosting as follows: This process is repeated T times, and a model W T , which consists of T types of Semi-PERbased models, is obtained.
in test phase, the segments and labels of a word sequence X is decided as follows: Since our algorithm handles real valued scores of samples given by Semi-PER on the exponential loss of AdaBoost, it's difficult to analytically determine a confidence-value α t that satisfies Z t (α t ) < 1 at boosting round t. we use a bisection search to find a confidence-value.