An information processing apparatus comprising: a first obtaining unit (20, 22-0DB) configured to obtain, for a first task that classifies between a normal and an abnormal situation in a first monitoring target in an image as a target task, a plurality of learning data, as target task learning data, which belongs to a first category that corresponds to the normal situation; a second obtaining unit (21, 22-1DB) configured to obtain, for a second task that classifies between a normal and an abnormal situation in a second monitoring target in an image and is different from the first task, a plurality of learning data, as source task learning data, including learning data which belongs to the first category, and learning data which belongs to a second category, which is different from the first category and corresponds to the abnormal situation; an adjusting unit configured to adjust a transformation parameter for transforming a feature amount of the source task learning data so as to minimize a difference between a first distribution of feature amounts of the plurality of learning data which belongs to the first category among the target task learning data and a second distribution of the feature amounts of the plurality of learning data which belongs to the first category among the source task learning data after transformation and so that the source task learning data can be used as the target task learning data, wherein the difference between the first and the second distribution is estimated based on respective feature amounts of the learning data of the first category of the target task and the source task; a transforming unit configured to transform at least the feature amount of the learning data which belongs to the second category of the source task learning data, on the basis of the transformation parameter adjusted by the adjusting unit; and a generating unit configured to generate a classifier regarding the first task on the basis of the feature amount of the target task learning data and the feature amount of at least the learning data which belongs to the second category of the source task learning data, transformed by the transforming unit, wherein the source and target task learning data is image data and the classifier is generated to discriminate image data which belongs to the first category from image data which belongs to the second category. a first obtaining unit (20, 22-0DB) configured to obtain, for a first task that classifies between a normal and an abnormal situation in a first monitoring target in an image as a target task, a plurality of learning data, as target task learning data, which belongs to a first category that corresponds to the normal situation; a second obtaining unit (21, 22-1DB) configured to obtain, for a second task that classifies between a normal and an abnormal situation in a second monitoring target in an image and is different from the first task, a plurality of learning data, as source task learning data, including learning data which belongs to the first category, and learning data which belongs to a second category, which is different from the first category and corresponds to the abnormal situation; an adjusting unit configured to adjust a transformation parameter for transforming a feature amount of the source task learning data so as to minimize a difference between a first distribution of feature amounts of the plurality of learning data which belongs to the first category among the target task learning data and a second distribution of the feature amounts of the plurality of learning data which belongs to the first category among the source task learning data after transformation and so that the source task learning data can be used as the target task learning data, wherein the difference between the first and the second distribution is estimated based on respective feature amounts of the learning data of the first category of the target task and the source task; a transforming unit configured to transform at least the feature amount of the learning data which belongs to the second category of the source task learning data, on the basis of the transformation parameter adjusted by the adjusting unit; and a generating unit configured to generate a classifier regarding the first task on the basis of the feature amount of the target task learning data and the feature amount of at least the learning data which belongs to the second category of the source task learning data, transformed by the transforming unit, wherein the source and target task learning data is image data and the classifier is generated to discriminate image data which belongs to the first category from image data which belongs to the second category.