1. A data processing method for performing learning on a decision tree used in a discriminator which discriminates an input image, the data processing method comprising: setting a plurality of values including at least two positive values, for a plurality of learning images having respective attributes, as respective selection probabilities for the plurality of learning images; setting an initial set of the plurality of learning images to an uppermost node of a plurality of nodes in the decision tree; sequentially selecting a node of the plurality of nodes other than a leaf node in the decision tree from the uppermost node; selecting a sample subset of the learning images from a set of learning images assigned to the selected node in accordance with selection probabilities of the learning images; determining a branch rule for dividing the set of the learning images assigned to the selected node into at least two subnode subsets using the selected sample subset of the learning images; updating the selection probabilities of the learning images by decreasing the selection probabilities for the selected sample subset of the learning images, wherein the selection probabilities are greater than zero and less than one; and assigning the subnode subsets divided in accordance with the branch rule to nodes succeeding to the selected node as respective sets for the succeeding nodes.