1. A data learning apparatus comprising: first learning means for deriving a temporary self-organizing map in which classes are associated with respective vector points of reference feature vectors by learning first learning data including a plurality of first sample feature vectors for each of which a corresponding class is known; and second learning means for modifying the temporary self-organizing map and deriving a final self-organizing map by learning second learning data including a plurality of second sample feature vectors for each of which a corresponding class is known; wherein the second learning means includes: second vector specifying means for reading one of the second sample feature vectors out of the second learning data and specifying a second learning winner vector on the temporary self-organizing map which has the highest similarity to said one of the second sample feature vectors; modification means for comparing a class associated with a vector point of the second learning winner vector to a corresponding class of said one of the second sample feature vectors indicated by the second learning data and, when the class associated with the vector point of the second leaning winner vector is not identical to the corresponding class indicated by the second learning data, modifying the second learning winner vector and a plurality of reference feature vectors distributed in a second learning vicinity of the second learning winner vector on the temporary self-organizing map so as to reduce the similarity thereof to said one of the second sample feature vectors; and means for deriving the final self-organizing map by operating each of the second vector specifying means and the modification means once or repeatedly more than once for each of said plurality of second sample feature vectors.