A classification apparatus includes: a calculation unit that outputs, as a classification result, results of classification by each of a plurality of classifiers with respect to learning data formed of data of at least two classes at a learning time and calculates a combination result value obtained by linear combination, using a combination coefficient, of results of classification by each of the plurality of classifiers with respect to the learning data to output the calculated combination result value as the classification result at a classification time; an extraction unit that extracts a correct solution class and an incorrect solution class for each of the classifiers from the classification result; a difference calculation unit that calculates a difference between the correct solution class and the incorrect solution class for each of the classifiers; a conversion unit that calculates a feature vector using the calculated difference for each of the classifiers; and a combination coefficient setting unit that learns, using a support vector machine, a value based on the feature vector with respect to a plurality of the learning data and thereby updates the combination coefficient, wherein input data is classified using the updated combination coefficient.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A classification apparatus comprising: a calculation unit that outputs, as a classification result, results of classification by each of a plurality of classifiers with respect to learning data formed of data of at least two classes at a learning time and calculates a combination result value obtained by linear combination, using a combination coefficient, of results of classification by each of the plurality of classifiers with respect to the learning data formed of data of at least two classes to output the calculated combination result value as the classification result at a classification time; an extraction unit that extracts a correct solution class and an incorrect solution class other than the correct solution class for each of the classifiers from the classification result; a difference calculation unit that calculates a difference between the correct solution class and the incorrect solution class for each of the classifiers; a conversion unit that calculates a feature vector using the calculated difference for each of the classifiers; and a combination coefficient setting unit that learns, using a support vector machine, a value based on the feature vector with respect to a plurality of the learning data and thereby updates the combination coefficient, wherein input data is classified using the updated combination coefficient. 2. The classification apparatus according to claim 1, wherein the extraction unit extracts all the incorrect solution classes from the classification result, and the difference calculation unit calculates a difference between the correct solution class and all the incorrect solution classes for each of the classifiers and separately for each class. 3. The classification apparatus according to claim 1, wherein the calculation unit outputs, as the classification result, a combination result value obtained by linear combination, using a combination coefficient, of results of classification by each of the plurality of classifiers with respect to the learning data formed of data of at least two classes at a learning time, the extraction unit extracts a maximum value of the classification result other than the correct solution class from the classification result, and the difference calculation unit calculates a difference between a classification result of learning data in the correct solution class and a classification result of learning data in a class in which the classification result is maximum in the incorrect solution class for each of the classifiers. 4. The classification apparatus according to claim 1, wherein the conversion unit converts the calculated feature vector to the value based on the feature vector by adding a +1 label that indicates a correct solution class to the calculated feature vector, and the combination coefficient setting unit updates the combination coefficient to a combination coefficient at which a margin between a classification boundary and a support vector is maximized. 5. The classification apparatus according to claim 4, wherein the classification boundary passes through an origin. 6. The classification apparatus according to claim 4, wherein an initial value of the combination coefficient is one, and the combination coefficient setting unit repeats learning until the margin between the classification boundary and the support vector converges to update the combination coefficient. 7. The classification apparatus according to claim 1, wherein the learning data and the input data are a speech signal that includes a word representing a command of motion and a noun, and each of the plurality of classifiers is a classifier that performs speech recognition, a classifier that classifies a word representing a motion included in a result of the speech recognition, a classifier that classifies a noun included in the result of the speech recognition, and a classifier that classifies whether or not the word representing the motion and the noun have been extracted without excess and deficiency. 8. A robot comprising a classification apparatus according to claim 1, wherein a speech signal of the learning data is a speech signal of a command that prompts a motion of the robot. 9. A classification method comprising: (a) by way of a calculation unit, outputting, as a classification result, results of classification by each of a plurality of classifiers with respect to learning data formed of data of at least two classes at a learning time and calculating a combination result value obtained by linear combination, using a combination coefficient, of results of classification by each of the plurality of classifiers with respect to the learning data formed of data of at least two classes to output the calculated combination result value as the classification result at a classification time; (b) by way of an extraction unit, extracting a correct solution class and an incorrect solution class other than the correct solution class for each of the classifiers from the classification result; (c) by way of a difference calculation unit, calculating a difference between the correct solution class and the incorrect solution class for each of the classifiers; (d) by way of a conversion unit, calculating a feature vector using the calculated difference for each of the classifiers; (e) by way of the conversion unit, calculating a feature vector using the calculated difference for each of the classifiers and converting the calculated feature vector to a value based on the feature vector by adding a +1 label that indicates a correct solution class to the calculated feature vector; (f) by way of a combination coefficient setting unit, learning, using a support vector machine, the value based on the feature vector with respect to a plurality of the learning data and thereby updating the combination coefficient to a combination coefficient at which a margin between a classification boundary that passes through an origin and a support vector is maximized.