A learning method includes: counting any one of or some of the number of labels added to each of feature amount vectors included in a learning data set, the number of types of the label, the number of feature amount vectors added with the same label, and the number of data pairs used for learning of a hyperplane, by a processor; first selecting, according to a result of the counting, one or more generation methods from a plurality of previously stored generation methods that generate the data pairs from the learning data set, by the processor; generating, using the selected generation methods, the data pairs from the feature amount vectors included in the learning data set, by the processor; and first learning, using the generated data pairs, the hyperplane that divides a feature amount vector space, by the processor.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning method comprising: counting any one of or some of the number of labels added to each of feature amount vectors included in a learning data set, the number of types of the label, the number of feature amount vectors added with the same label, and the number of data pairs used for learning of a hyperplane, by a processor; first selecting, according to a result of the counting, one or more generation methods from a plurality of previously stored generation methods that generate the data pairs from the learning data set, by the processor; generating, using the selected generation methods, the data pairs from the feature amount vectors included in the learning data set, by the processor; and first learning, using the generated data pairs, the hyperplane that divides a feature amount vector space, by the processor. 2. The learning method according to claim 1, further including: first calculating, using the counting result, a ratio of the number of the data pairs to be generated by each of the generation methods to the total number of data pairs to be used, by the processor; second selecting, at the calculated ratios, the data pairs generated by the generation methods, by the processor; and second learning the hyperplane using the selected data pairs, by the processor. 3. The learning method according to claim 2, further including third selecting the data pair generation methods based on a ratio of the feature amount vectors added with one label, a ratio according to the number of the types of the label, a ratio according to the number of the feature amount vectors added with the same label, and a ratio according to the number of the data pairs used in learning of the hyperplane, by the processor. 4. The learning method according to claim 1, further including: second calculating the ratio of the number of the data pairs to be generated by each of the generation methods to the total number of data pairs to be selected by inputting, to a function whose value is defined by a first argument and a second argument, in which the value for the first argument is monotonously non-decreasing such that the value becomes 0 when the first argument is negative infinity and becomes 1 when the first argument is positive infinity and in which a lower limit of the first argument for which the value is equal to or larger than ½ is defined as the second argument, the counting result and a predetermined threshold serving as the first argument or second argument, by the processor; fourth selecting the data pairs generated by the generation methods at the calculated ratios, by the processor; and third learning the hyperplane using the selected data pairs, by the processor. 5. The learning method according to claim 1, further including fifth selecting one or more generation methods that generates the data pairs for learning the hyperplane from among first to fifth generation methods, by the processor, the first generation method being a method that generates a pair obtained by randomly selecting from the feature amount vectors added with the same label and a pair obtained by randomly selecting feature amount vectors added with different labels, the second generation method being a method that generates a pair of a feature amount vector that is most distant from a feature amount vector selected as a reference among feature amount vectors added with the same label as the label of the feature amount vector serving as a reference and the feature amount vector serving as a reference and a pair of a feature amount vector that is closest to the feature amount vector serving as a reference among feature amount vectors added with a different label from the label of the feature amount vector serving as a reference and the feature amount vector serving as a reference, the third generation method being a method that generates a pair obtained by randomly selecting from the feature amount vectors added with the same label and a pair of a feature amount vector that is closest to a feature amount vector selected as a reference among feature amount vectors added with a different label from the label of the feature amount vector serving as a reference and the feature amount vector serving as a reference, the fourth generation method being a method that generates a pair obtained by randomly selecting from the feature amount vectors added with the same label and a pair of a reference vector added with a different label from those of feature amount vectors included in a sample set obtained by randomly selecting one or more feature amount vectors and having a minimum generalized mean in terms of distance from the feature amount vectors included in the sample set and a feature amount vector added with a different label from the label of the reference vector, the fifth generation method being a method that generates a pair of a feature amount vector that is closest to a feature amount vector selected as a reference among feature amount vectors added with the same label as the label of the feature amount vector serving as a reference and the feature amount vector serving as a reference and a pair of a feature amount vector that is closest to the feature amount vector serving as a reference among feature amount vectors added with a different label from the label of the feature amount vector serving as a reference and the feature amount vector serving as a reference. 6. The learning method according to claim 5, wherein for the feature amount vectors each added with one label, the number of data pairs generated by the first generation method or second generation method is increased as the number of feature amount vectors added with the same label is smaller and the number of feature amount vectors added with the same label is larger, for the feature amount vectors each added with one label, the number of data pairs generated by the third generation method is increased as the number of feature amount vectors added with the same label is larger and the number of feature amount vectors added with the same label is smaller, for the feature amount vectors each added with a plurality of labels, the number of data pairs generated by the fourth generation method is increased as the number of feature amount vectors added with the same label is larger, the number of feature amount vectors added with the same label is smaller, and the number of data pairs used in learning the hyperplane is larger, and the data pairs generated by the fifth generation method are selected as the remainder data pairs. 7. An information conversion device comprising: a processor configured to execute a process including: generating, using different generation methods, data pairs from feature amount vectors included in a learning data set; counting any one of or some of the number of labels added to each of the feature amount vectors included in the learning data set, the number of types of the label, the number of feature amount vectors added with the same label, and the number of data pairs used for learning of a hyperplane; selecting, according to a result of the counting, one or more generators that generate the data pairs from the learning data set; and learning, using the data pairs generated by the generators selected at the selecting, the hyperplane that divides a feature amount vector space. 8. A non-transitory computer-readable recording medium storing a learning program that causes a computer to execute a process comprising: counting any one of or some of the number of labels added to each of feature amount vectors included in a learning data set, the number of types of the label, the number of feature amount vectors added with the same label, and the number of data pairs used for learning of a hyperplane; selecting, according to a result of the counting, one or more generation methods from a plurality of previously stored generation methods that generate the data pairs from the learning data set; generating, using the selected generation methods, the data pairs from the feature amount vectors included in the learning data set; and learning, using the generated data pairs, the hyperplane that divides a feature amount vector space.