Abstract: A lot of classification algorithms get the similarity between samples according to their distance．Therefore,for this kind of algorithms,the way for getting distance is very important．Studies in existing metric or similarity learning algorithms find that most of the existing methods take use of random samples from training database for learning．This sampling method gives an equal probability for every training samples to be used for metric learning．However,the different location results in different classification difficulty of samples．If those samples who are difficult classified could be used more frequently in learning,while other samples arranged less learning time,the efficiency of learning will be improved．Reducing learning time is significant in Big-Data era.

Key words: Similarity measurement,Distance metric,Weighted sampling,Machine learning,k-NN,Boosting