In　this　paper,　we　propose　an　active　sample　selection　algorithm　(SSME)　based　on　maximum　entropy　criterion.　By　calculating　the　information　entropy　of　the　unlabeled　samples,　the　algorithm　can　find　the　most　informative　samples　from　unlabeled　data　set.　Comparative　experiments　with　random　selection　algorithm　are　conducted　on　10　real　data　sets.　The　results　show　the　superiority　of　our　proposed　algorithm　in　terms　of　predictive　accuracy　and　condensing　rate.　©　2012　IEEE.