A machine learning apparatus (100) including: a feature calculation unit (11) that transforms, into first numerical data sets, training data sets to each of which either one of two values is added; a support vector machine learning unit (21) that learns, based on the first numerical data sets, a criterion for classification of the two values, creating a learning model; a self-organizing map learning unit (22) that projects the first numerical data sets onto a two-dimensional map, the two-dimensional map having blocks and representative data sets, wherein the self-organizing map learning unit (22) causes, first numerical data sets with a short distance from each other to belong to adjacent blocks; a support vector machine classifying unit (25) that classifies, by using the learning model, the blocks and the representative data sets; and a learning model two-dimensionalization unit (31) that creates a two-dimensional learning model representing the results of the classification.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning apparatus comprising: a feature calculation unit that transforms, into first numerical data sets, training data sets to each of which either one of two values is added as a label, each of the first numerical data sets containing a numerical value representing a feature of the corresponding training data set; a support vector machine learning unit that learns, based on the first numerical data sets obtained by the transformation of the training data sets, and by using a support vector machine, a criterion for classification of the two values in the label, thereby creating a learning model representing the results of the learning; a self-organizing map learning unit that projects the first numerical data sets onto a two-dimensional map by self-organizing map processing, the two-dimensional map having blocks arranged in a matrix and having representative data sets belonging to the blocks, wherein the self-organizing map learning unit causes, from among the first numerical data sets, two or more first numerical data sets with a short distance from each other to belong to adjacent blocks among the blocks of the two-dimensional map; a support vector machine classifying unit that classifies, by using the learning model, the blocks of the two-dimensional map, onto which the first numerical data sets have been projected, and the representative data sets; and a learning model two-dimensionalization unit that creates a two-dimensional learning model representing the results of the classification. 2. The machine learning apparatus according to claim 1 further comprising: a self-organizing map classifying unit that specifies, by using the two-dimensional map, the blocks corresponding to the first numerical data sets; and a training data two-dimensionalization unit that creates two-dimensional training data sets that associate the first numerical data sets with the blocks specified by the self-organizing map classifying unit. 3. The machine learning apparatus according to claim 2 further comprising a training candidate data two-dimensionalization unit, wherein the feature calculation unit transforms, into second numerical data sets, training candidate data sets to which the label is not added, each of the second numerical data sets containing a numerical value representing a feature of the corresponding training candidate data set, the self-organizing map learning unit specifies, by using the two-dimensional map, the blocks corresponding to the second numerical data sets, and the training candidate data two-dimensionalization unit creates two-dimensional training candidate data sets that associate the second numerical data sets with the blocks specified by the self-organizing map learning unit. 4. The machine learning apparatus according to claim 3 further comprising: a data synthesizing unit that creates synthesized two-dimensional data sets by combining the two-dimensional learning model, which represents the results of the classification, with the two-dimensional training data sets and the two-dimensional training candidate data sets; and a synthesized two-dimensional data presentation unit that displays, on a screen, the blocks of the two-dimensional learning model representing the results of the classification, based on the synthesized two-dimensional data sets, wherein, the synthesized two-dimensional data presentation unit displays, for each of the blocks, the results of the classification, the number of the first numerical data sets associated with the corresponding block and the labels added to the first numerical data sets associated with the corresponding block. 5. The machine learning apparatus according to claim 4, wherein, when any block is selected from among the blocks displayed on the screen, the synthesized two-dimensional data presentation unit specifies the first numerical data sets and the second numerical data sets associated with the selected block, and displays, on the screen, original training data sets and original training candidate data sets from which the first numerical data sets and the second numerical data sets have been created respectively by the transformation. 6. The machine learning apparatus according to claim 5 further comprising a training data improving unit, wherein the training data improving unit compares the first numerical data sets associated with a target block and the first numerical data sets associated with blocks located around the target block, and, based on the result of the comparison, the training data improving unit displays, on the screen, an instruction to delete original training data sets from which the first numerical data sets associated with the target block have been created by the transformation, or an instruction to correct the labels added to the original training data sets. 7. The machine learning apparatus according to claim 6, wherein, when the number of the first numerical data sets associated with the target block is no greater than a threshold value, the training data improving unit displays, on the screen, original training candidate data sets from which the second numerical data sets associated with the target block have been created by the transformation, and displays an instruction to add the original training candidate data sets to the training data sets. 8. A machine learning method comprising: (a) a step of transforming, into first numerical data sets, training data sets to each of which either one of two values is added as a label, each of the first numerical data sets containing a numerical value representing a feature of the corresponding training data set; (b) a step of learning, based on the first numerical data sets obtained by the transformation of the training data sets, and by using a support vector machine, a criterion for classification of the two values in the label, thereby creating a learning model representing the results of the learning; (c) a step of projecting the first numerical data sets onto a two-dimensional map by self-organizing map processing, the two-dimensional map having blocks arranged in a matrix and having representative data sets belonging to the blocks, wherein the projection is performed such that, from among the first numerical data sets, two or more first numerical data sets with a short distance from each other belong to adjacent blocks or a same block among the blocks of the two-dimensional map; (d) a step of classifying, by using the learning model created in the step (b), the blocks of the two-dimensional map and the representative data sets; and (e) a step of creating a two-dimensional learning model representing the results of the classification performed in the step (d). 9. A non-transitory computer-readable recording medium that stores a program including an instruction for causing a computer to perform: (a) a step of transforming, into first numerical data sets, training data sets to each of which either one of two values is added as a label, each of the first numerical data sets containing a numerical value representing a feature of the corresponding training data set; (b) a step of learning, based on the first numerical data sets obtained by the transformation of the training data sets, and by using a support vector machine, a criterion for classification of the two values in the label, thereby creating a learning model representing the results of the learning; (c) a step of projecting the first numerical data sets onto a two-dimensional map by self-organizing map processing, the two-dimensional map having blocks arranged in a matrix and having representative data sets belonging to the blocks, wherein the projection is performed such that, from among the first numerical data sets, two or more first numerical data sets with a short distance from each other belong to adjacent blocks or a same block among the blocks of the two-dimensional map; (d) a step of classifying, by using the learning model created in the step (b), the blocks of the two-dimensional map and the representative data sets; and (e) a step of creating a two-dimensional learning model representing the results of the classification performed in the step (d).