There is provided a classifier capable of classifying unknown abnormal data input to the classifier even if there is a small number of abnormal data used for the learning of the classifier. When learning parameters of the classifier, the specific category likelihood of normal patterns for learning relatively deviating from a group of normal patterns for learning is decreased relatively to the specific category likelihood of normal patterns for learning not relatively deviating from the group of normal patterns for learning, and the specific category likelihood of abnormal patterns for learning is decreased relatively to the specific category likelihood of the group of normal patterns for learning.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A classifier generation apparatus configured to generate a classifier for calculating a value representing a specific category likelihood for input data based on a predetermined parameter and identifying whether the input data is data belonging to the specific category based on a result of the calculation, the classifier generation apparatus comprising: an input unit configured to input a plurality of specific category data for learning known to belong to a specific category and at least one piece of non-specific category data for learning known not to belong to the specific category; an acquisition unit configured to acquire a feature amount based on the plurality of specific category data for learning and the at least one piece of non-specific category data for learning; and a determination unit configured to determine the parameter based on the acquired feature amount, wherein the determination unit determines the parameter so that, with a decrease in the specific category likelihood of data relatively deviating from a distribution of the plurality of specific category data for learning known to belong to the specific category, the likelihood of the at least one piece of non-specific category data for learning known not to belong to the specific category becomes relatively smaller than the likelihood of the specific category data for learning. 2. The classifier generation apparatus according to claim 1, wherein the determination unit determines the parameter based on a result of calculation in which, when the specific category data for learning deviates from the distribution of the plurality of specific category data for learning, the likelihood for the specific category data for learning is set to the specific category unlikelihood, and the likelihood for the non-specific category data for learning is set to a value indicating the specific category unlikelihood instead of the value representing the specific category likelihood for the specific category data for learning. 3. The classifier generation apparatus according to claim 1, wherein the determination unit determines the parameter based on a result of a calculation using a specific category learning term for calculating the likelihood for the specific category data for learning and a non-specific category learning term for calculating the likelihood for the non-specific category data for learning. 4. The classifier generation apparatus according to claim 3, wherein at least one of the specific category learning term and the non-specific category learning term is a term including a formula including the parameter for calculating the likelihood, and wherein the determination unit determines the parameter for maximizing or minimizing a value of a target function for maximizing or minimizing the likelihood, the target function including the specific category learning term and the non-specific category learning term. 5. The classifier generation apparatus according to claim 1, wherein the determination unit further comprises: an initialization unit configured to initialize the parameter; and a correction unit configured to correct the parameter initialized by the initialization unit, by using the plurality of specific category data for learning and at least one of the non-specific category data for learning, wherein the correction unit corrects the parameter initialized by the initialization unit so that, when the specific category data for learning deviates from the distribution of a plurality of the specific category data for learning, a value indicating the specific category likelihood for the specific category data for learning becomes a value indicating the specific category unlikelihood, and a value representing the specific category likelihood for the non-specific category data for learning becomes a value indicating the specific category unlikelihood instead of the value representing the specific category likelihood for the specific category data for learning. 6. The classifier generation apparatus according to claim 1, wherein the formula including the parameter for calculating the likelihood is a formula including a difference between data subjected to calculation of the value and the specific category data for learning, and wherein the determination unit determines the parameter according to the difference between the non-specific category data for learning and the specific category data for learning. 7. The classifier generation apparatus according to claim 1, wherein, when the parameter is changed so that the specific category data for learning deviates from the distribution of the plurality of specific category data for learning, the non-specific category data for learning deviates from the distribution of the plurality of specific category data for learning. 8. The classifier generation apparatus according to claim 1, further comprising: a division unit configured to divide the plurality of specific category data for learning into a plurality of sets; and a derivation unit configured to derive a hyper parameter based on a subset which is a plurality of the sets excluding at least one set, the non-specific category data for learning, and a candidate of a hyper parameter used to determine the parameter, wherein the derivation unit determines the parameter based on the subset, the non-specific category data for learning, and the candidate of the hyper parameter by using the same method as the method for determining the parameter by the determination unit, evaluates a result of the determination based on sets different from the subset out of the plurality of the sets and the non-specific category data for learning, and derives the hyper parameter based on a result of the evaluation. 9. A classifier comprising: a calculation unit configured to calculate the likelihood for input data by using the parameter determined by the classifier generation apparatus according to claim 1; and an identification unit configured to identify whether the input data is data belonging to the specific category based on a value calculated by the calculation unit. 10. A classifier generation method for generating a classifier for calculating a value representing a specific category likelihood to input data based on a predetermined parameter and identifying whether the input data is data belonging to the specific category based on a result of the calculation, the classifier generation method comprising: inputting a plurality of specific category data for learning known to belong to a specific category and at least one piece of non-specific category data for learning known not to belong to the specific category; acquiring a feature amount based on the plurality of specific category data for learning and the non-specific category data for learning; and determining the parameter based on the acquired feature amount, wherein the parameter is determined so that, with a decrease in the specific category likelihood of data relatively deviating from a distribution of the plurality of specific category data for learning known to belong to the specific category, the likelihood of the at least one piece of non-specific category data for learning known not to belong to the specific category becomes relatively smaller than the likelihood of the specific category data for learning. 11. A non-transitory computer-readable storage medium storing a program for causing a computer to function as a classifier generation apparatus for generating a classifier for calculating a value representing a specific category likelihood for input data based on a predetermined parameter and identifying whether the input data is data belonging to the specific category based on a result of the calculation, the classifier generation apparatus comprising: an input unit configured to input a plurality of specific category data for learning known to belong to a specific category and at least one piece of non-specific category data for learning known not to belong to the specific category; an acquisition unit configured to acquire a feature amount based on the plurality of specific category data for learning and the at least one piece of non-specific category data for learning; and a determination unit configured to determine the parameter based on the acquired feature amount, wherein the determination unit determines the parameter so that, with a decrease in the specific category likelihood of data relatively deviating from a distribution of the plurality of specific category data for learning known to belong to the specific category, the likelihood of the at least one piece of non-specific category data for learning known not to belong to the specific category becomes relatively smaller than the likelihood of the specific category data for learning.