Identifying and Correcting Mislabled Training Instances Using Bayes
基于Bayes的有噪训练集去噪方法研究

De-noising is a basic pretreatment in the process of training a classifier.Most traditional de-noising approaches only delete instances tagged as noise which obviously also eliminates the useful information in these instances.A new approach is presented with which we can not only identify noise but also correct it,so that the useful information will be reserved.