Detection learning device, method, and program

A detection learning method comprising:

• setting, by a region-to-be-maximized setter so as to narrow, at each repetition, a range determined by an upper limit and a lower limit of a true positive rate for defining a part of an area under a Receiver Operating Characteristic (ROC) curve on a graph representing a correspondence between the true positive rate that is probability of correctly classifying positive example data as a positive example and a false positive rate that is probability of incorrectly classifying negative example data as the positive example;
• training, by a maximization learner, a score function so as to optimize an objective function represented using positive example data selected from ranked positive example data, negative example data, and the score function that calculates a score representing likelihood of a positive example according to the set range between the upper limit and the lower limit of the true positive rate;
• ranking, by a ranker, the positive example data based on the score calculated using the score function;
• and
• causing, by a determiner, causing the maximization learner and the ranker to repeat the processing until the objective function is converged, and the region-to-be-maximized setter to repeat setting until the range between the upper limit and the lower limit of the true positive rate becomes a predetermined size.

A detection learning method comprising:

• setting, by a region-to-be-maximized setter, so as to narrow, at each repetition, a range determined by an upper limit and a lower limit of a false positive rate for defining a part of an area under a Receiver Operating Characteristic (ROC) curve on a graph representing a correspondence between a true positive rate that is probability of correctly classifying positive example data as a positive example and the false positive rate that is probability of incorrectly classifying negative example data as the positive example;
• training, by a maximization learner, a score function so as to optimize an objective function represented using negative example data selected from ranked negative example data, positive example data, and the score function that calculates a score representing likelihood of a positive example according to the set range between the upper limit and the lower limit of the false positive rate;
• ranking, by a ranker, the negative example data based on the score calculated using the score function;
• and
• causing, by a determiner, the maximization learner and the ranker to repeat the processing until the objective function is converged, and the region-to-be-maximized setter to repeat setting until the range between the upper limit and the lower limit of the false positive rate becomes a predetermined size.