A learning apparatus includes: an update unit which updates a dictionary used by a classifier; a calculation unit which calculates, by using a dictionary updated and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and a determination unit which determines whether to update the dictionary, by using the loss, wherein, when the determination unit determines to update the dictionary, the update unit updates the dictionary by using the samples with labeling added with a new sample with labeling, and wherein the determination unit determines whether to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning apparatus comprising: an update unit configured to update a dictionary used by a classifier; a calculation unit configured to calculate, by using a dictionary updated by the update unit and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and a determination unit configured to determine whether or not to update the dictionary, by using the loss, wherein, when the determination unit determines to update the dictionary, the update unit updates the dictionary by using the samples with labeling added with a new sample with labeling, and wherein the determination unit determines whether or not to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling. 2. The learning apparatus according to claim 1, wherein, when the loss calculated by using the updated dictionary decreases in inverse proportion to a number of the samples with labeling, the determination unit determines not to update the dictionary. 3. The learning apparatus according to claim 2, wherein, when the loss calculated by using the updated dictionary is less than a past loss calculated by the calculation unit when a number of the samples with labeling is smaller by one, the determination unit determines not to update the dictionary. 4. The learning apparatus according to claim 2, wherein, when an average of the loss calculated by using the updated dictionary and a predetermined number of past losses is less than an average of a predetermined number of losses calculated by the calculation unit before the predetermined number of past losses are calculated, the determination unit determines not to update the dictionary. 5. The learning apparatus according to claim 2, wherein the determination unit calculates a correlation function between a ratio of a number of the samples with labeling to a first number of samples being smaller than the number of the samples with labeling by a predetermined number, and a ratio of a loss when a number of the samples with labeling is the first number of samples to a loss with respect to all the samples with labeling, and, when the correlation function is greater than a predetermined threshold value, determines not to update the dictionary. 6. The learning apparatus according to claim 1, further comprising: a selection unit configured to select a sample that is likely to be discriminated as a class not being a correct-answer class, from one or more samples not assigned with a label, as a sample being a target of labeling; and an acquisition unit configured to acquire, when a label is assigned to the sample being a target of labeling selected by the selection unit, the samples with labeling including the sample being a target of labeling, wherein the update unit updates the dictionary by using the samples with labeling acquired by the acquisition unit. 7. The learning apparatus according to claim 1, further comprising an output unit configured to output the dictionary when the determination unit determines not to update the dictionary. 8. (canceled) 9. A learning method comprising: updating a dictionary used by a classifier; by using the updated dictionary and one or more samples with labeling being samples assigned with labels, calculating a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; determining whether or not to update the dictionary, by using the loss; when determining to update the dictionary, updating the dictionary by using samples with labeling added with a new piece of the samples with labeling; by using the updated dictionary and the samples with labeling added with the new sample with labeling, calculating a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and determining whether or not to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling. 10. A computer-readable non-transitory recording medium storing a program causing a computer to perform: processing of updating a dictionary used by a classifier; processing of calculating, by using the updated dictionary and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and processing of determining whether or not to update the dictionary, by using the loss, wherein, when determining to update the dictionary, the processing of updating the dictionary is processing of updating the dictionary by using the samples with labeling added with a new piece of the samples with labeling, and wherein the processing of determining whether or not to update the dictionary is processing of determining whether or not to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling.