1. A learning system comprising: an input section which obtains learning data to which labels are set and an end condition; a learning section which learns said learning data through an ensemble learning by using a learning algorithm to generate hypotheses; a storage section in which a plurality of candidate data having no label are stored; a calculating section which carries out an averaging with weights to said plurality of hypotheses, refers to said storage section and calculates a score for each of said plurality of candidate data by using said hypotheses; a selecting section which selects desired candidate data from among said plurality of candidate data based on the calculated scores, said selecting section having a previously set stochastic selection function; a data updating section which sets a label determined by a user to said desired candidate data and adds said desired candidate data to said learning data and outputs to said learning section; an output unit; and a control unit which outputs said hypotheses generated by said learning section to said output unit when said end condition is met, wherein said learning section re-samples said learning data to generate partial data by said ensemble learning, and re-samples an attribute of said learning data to generate a partial attribute, and learns said learning data based on said partial data and said partial attribute, wherein said calculated scare has a numeric value of a likelihood of a positive example of each candidate data.