An information processing apparatus comprising: a generating means for generating, from a data group including a plurality of training data elements given different correct labels, a plurality of training data sets in such a manner that the number of included training data elements changes successively, by selecting one or more training data elements from the data group; and a training means for repeatedly training a learning model for machine learning by successively applying the plurality of training data sets to the learning model in an time-series order in which the plurality of training data sets were generated, wherein change in the number of training data elements includes an increase and a decrease in the number of training data elements by randomly selecting a predetermined fraction of the training data elements from the training data group as an initial training data set in the time-series order, and for each subsequent training data set in the time-series order, randomly adding, to a previous training data set, training data elements not included in the previous training data set and/or randomly deleting, from the previous training data set, training data elements, and wherein the generating means generates the plurality of training data sets from the plurality of training data elements in such a manner that the number of training data elements successively changes from the previous training data set to the subsequent training data set. a generating means for generating, from a data group including a plurality of training data elements given different correct labels, a plurality of training data sets in such a manner that the number of included training data elements changes successively, by selecting one or more training data elements from the data group; and a training means for repeatedly training a learning model for machine learning by successively applying the plurality of training data sets to the learning model in an time-series order in which the plurality of training data sets were generated, wherein change in the number of training data elements includes an increase and a decrease in the number of training data elements by randomly selecting a predetermined fraction of the training data elements from the training data group as an initial training data set in the time-series order, and for each subsequent training data set in the time-series order, randomly adding, to a previous training data set, training data elements not included in the previous training data set and/or randomly deleting, from the previous training data set, training data elements, and randomly selecting a predetermined fraction of the training data elements from the training data group as an initial training data set in the time-series order, and for each subsequent training data set in the time-series order, randomly adding, to a previous training data set, training data elements not included in the previous training data set and/or randomly deleting, from the previous training data set, training data elements, and wherein the generating means generates the plurality of training data sets from the plurality of training data elements in such a manner that the number of training data elements successively changes from the previous training data set to the subsequent training data set.