求翻译：该方法是通过有放回抽样方式，从原始训练集中抽取若干示例组成各子分类器的训练集 ，每个训练样本集用来训练一个支持向量分类器，最后再用模糊积分对各个子SVM分类器进行融合。Bagging的方法通过重新选取训练集增加了分类器集成的差异度，从而提高了泛化能力。是什么意思？

This method is sampling with replacement from the original training set composed of various sub-sample taken a number of classifier training set, each training sample set used to train a support vector classifier, fuzzy integral and then the final SVM classification for the various sub- fusion devic

The method is to use random, there is a back from the original training concentrated on samples comprising several examples of each of these sub-classifications server training set, each training sample sets used to train a support vector classifier, and finally use a vague points of various sub-SVM

This method is through has the sampling with replacement way, extracts certain demonstrations from the primitive training regulations to compose each sub-sorter the training regulations, each training sample collection uses for to train a support vector sorter, finally uses the fuzzy integral to car

The method is by means of sampling with replacement, extracted some examples from the original training set up subcategories for the training set, each set of training samples used to train a support vector classifier, and finally using fuzzy integral to each child for SVM classifier fusion. Bagging

This method is sampling with replacement from the original training set composed of various sub-sample taken a number of classifier training set, each training sample set used to train a support vector classifier, fuzzy integral and then the final SVM classification for the various sub- fusion devic