On the basis of a difference between first distribution regarding target task learning data as a plurality of learning data which belongs to a first category of a first task as a target task and second distribution regarding a plurality of learning data which belongs to the first category of source task learning data as learning data which belongs to the first category of a second task different from the first task, a transformation parameter for transforming the source task learning data is adjusted, the source task learning data is transformed based on the adjusted transformation parameter, and a classifier regarding the first task is generated based on the transformed source task learning data and the target task learning data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing apparatus comprising: a first obtaining unit configured to obtain, in a first task as a target task, a plurality of learning data, as target task learning data, which belongs to a first category; a second obtaining unit configured to obtain, in a second task different from the first task, a plurality of learning data, as source task learning data, including the learning data which belongs to the first category and learning data which belongs to a second category different from the first category; an adjusting unit configured to adjust a transformation parameter for transforming the source task learning data on the basis of a difference between first distribution regarding the plurality of learning data which belongs to the first category of the target task learning data and second distribution regarding the plurality of learning data which belongs to the first category of the source task learning data; a transforming unit configured to transform 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 source task learning data transformed by the transforming unit and the target task learning data. 2. The information processing apparatus according to claim 1, wherein the adjusting unit is configured to adjust the transformation parameter so that density distribution of the first distribution and density distribution of the second distribution are close. 3. The information processing apparatus according to claim 2, wherein the adjusting unit is configured to adjust the transformation parameter so as to locally minimize an estimation value of an L2 distance between the first distribution and the second distribution. 4. The information processing apparatus according to claim 2, wherein the adjusting unit is configured to adjust the transformation parameter so as to locally minimize an estimation value of a relative Piason distance between the first distribution and the second distribution. 5. The information processing apparatus according to claim 2, wherein the adjusting unit is configured to adjust the transformation parameter so as to locally minimize a difference between the first distribution and the second distribution by using a gradation method. 6. The information processing apparatus according to claim 1, further comprising a searching unit configured to search for the second task as a task similar to the first task, and wherein, the second obtaining unit is configured to obtain the learning data of the second task, as the source task learning data, searched for by the searching unit. 7. The information processing apparatus according to claim 1, wherein: the transforming unit is configured to transform the learning data which belongs to the second category in the source task learning data; and the generating unit is configured to generate the classifier by using the target task learning data and the learning data which belongs to the second category transformed by the transforming unit. 8. The information processing apparatus according to claim 1, wherein the generating unit is configured to select a feature amount for use in the generation of the classifier on the basis of the target task learning data and the source task learning data transformed by the transforming unit, and to generate the classifier on the basis of the selected feature amount. 9. The information processing apparatus according to claim 1, further comprising a classifying unit configured to classify whether or not input data as a target of the classification belongs to the first category by using the classifier generated by the generating unit. 10. An information processing method which is executed by an information processing apparatus, the method comprising: obtaining, in a first task as a target task, a plurality of learning data, as target task learning data, which belongs to a first category; obtaining, in a second task different from the first task, a plurality of learning data, as source task learning data, including the learning data which belongs to the first category and learning data which belongs to a second category different from the first category; adjusting a transformation parameter for transforming the source task learning data on the basis of a difference between first distribution regarding the plurality of learning data which belongs to the first category of the target task learning data and second distribution regarding the plurality of learning data which belongs to the first category of the source task learning data; transforming the source task learning data on the basis of the adjusted transformation parameter; and generating a classifier regarding the first task on the basis of the transformed source task learning data and the target task learning data. 11. A non-transitory computer-readable storage medium of storing a program for causing a computer to execute: a first obtaining step of obtaining, in a first task as a target task, a plurality of learning data, as target task learning data, which belongs to a first category; a second obtaining step of obtaining, in a second task different from the first task, a plurality of learning data, as source task learning data, including the learning data which belongs to the first category and learning data which belongs to a second category different from the first category; an adjusting step of adjusting a transformation parameter for transforming the source task learning data on the basis of a difference between first distribution regarding the plurality of learning data which belongs to the first category of the target task learning data and second distribution regarding the plurality of learning data which belongs to the first category of the source task learning data; a transforming step of transforming the source task learning data on the basis of the transformation parameter adjusted in the adjusting step; and a generating step of generating a classifier regarding the first task on the basis of the source task learning data transformed in the transforming step and the target task learning data.