A machine learning apparatus includes an analytical information storage unit that stores therein two or more pieces of analytical information each associating input/output information used for machine learning with time-point information indicating a time point of the input/output information, an analysis-object-set specifying unit that specifies an analysis object set containing a unit-period input-output set being a set of pieces of the input/output information corresponding to pieces of the time-point information indicating the time point in a unit period and an amount of the pieces of the input/output information of the set being dependent on a period between the time point of the unit periods and a specific time point, and a machine learning unit that performs machine learning by using the pieces of the input/output information contained in the analysis object set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning apparatus comprising: an analytical information storage unit that stores therein two or more pieces of analytical information each associating a piece of input/output information containing information on input and information on output used for machine learning with a piece of time-point information indicating a time point of the input/output information; an analysis-object-set specifying unit that specifies an analysis object set containing a unit-period input-output set for unit periods divided in a period, the unit-period input-output set being a set of pieces of the input/output information corresponding to pieces of the time-point information indicating the time point in the unit periods and an amount of the pieces of the input/output information of the set being dependent on a period between the time point of the unit periods and a specific time point; and a machine learning unit that performs machine learning by using the pieces of the input/output information contained in the analysis object set specified by the analysis-object-set specifying unit. 2. The machine learning apparatus according to claim 1, further comprising: a verifying unit that verifies a result of the machine learning performed by the machine learning unit, by using pieces of the input/output information other than the pieces of the input/output information used for the machine learning by the machine learning unit from among the pieces of the input/output information contained in the analysis object set specified by the analysis-object-set specifying unit, wherein the machine learning unit performs the machine learning by using some of the pieces of the input/output information contained in the analysis object set specified by the analysis-object-set specifying unit. 3. The machine learning apparatus according to claim 1, wherein the amount of the pieces of the input/output information contained in each of the unit-period input-output sets increases as the period between the time point of the unit period of the unit-period input-output set and the specific time point decreases. 4. The machine learning apparatus according to claim 1, wherein the amount of the pieces of the input/output information contained in each of the unit-period input-output sets periodically changes according to the period between the time point of the unit period of the unit-period input-output set and the specific time point. 5. The machine learning apparatus according to claim 2, wherein the analysis-object-set specifying unit specifies a plurality of the analysis object sets such that an amount of pieces of the input/output information contained in each of predetermined specific periods becomes the same between the plurality of the analysis object sets, and such that at least some of the pieces of the input/output information contained in at least any of the specific periods become different between the plurality of the analysis object sets, the machine learning unit performs the machine learning by using pieces of the input/output information other than pieces of verification information being pieces of the input/output information used for verification among the pieces of the input/output information contained in each of the analysis object sets specified by the analysis-object-set specifying unit, and the verifying unit verifies the result of each machine learning performed by the machine learning unit, by using the pieces of the verification information at the time of the machine learning. 6. The machine learning apparatus according to claim 5, wherein the analysis-object-set specifying unit specifies the plurality of the analysis object sets such that each of the pieces of the input/output information is contained at least once in any of the plurality of the analysis object sets. 7. The machine learning apparatus according to claim 2, wherein the analysis-object-set specifying unit specifies a plurality of the analysis object sets such that an amount of pieces of the input/output information contained in each of the unit periods becomes the same between the plurality of the analysis object sets, and such that each of the plurality of the analysis object sets contains a randomly-selected piece of the input/output information, the machine learning unit performs the machine learning by using pieces of the input/output information other than pieces of verification information being pieces of the input/output information used for verification among the pieces of the input/output information contained in each of the plurality of the analysis object sets specified by the analysis-object-set specifying unit, and the verifying unit verifies the result of each machine learning performed by the machine learning unit, by using the pieces of the verification information at the time of the machine learning. 8. The machine learning apparatus according to claim 5, further comprising: a class dividing unit that divides the pieces of the input/output information contained in each of the plurality of the analysis object sets specified by the analysis-object-set specifying unit into multiple classes corresponding to respective class periods divided in a period, wherein the pieces of the input/output information contained in each of the classes corresponds to pieces of the time-point information indicating time points in each of the class periods of the respective classes, and the machine learning unit repeats machine learning using pieces of the input/output information other than pieces of the verification information being pieces of the input/output information contained in a verification class that is a single class used for the verification, in such a manner that the classes corresponding to class periods that differ for each of the analysis object sets are employed as verification classes and until the classes corresponding to all of the class periods are employed as the verification classes. 9. The machine learning apparatus according to claim 2, further comprising: a class dividing unit that divides the pieces of the input/output information contained in the analysis object set specified by the analysis-object-set specifying unit into multiple classes corresponding to respective class periods divided in a period, wherein the pieces of the input/output information contained in each of the classes corresponds to pieces of the time-point information indicating the time point in each of the class periods of the respective classes, and the machine learning unit repeats machine learning using pieces of the input/output information contained in classes other than a verification class that is a single class used for verification in the analysis object set specified by the analysis-object-set specifying unit, in such a manner that all of the classes are employed as the verification classes, and the verifying unit verifies the result of each machine learning performed by the machine learning unit, by using the pieces of the input/output information contained in the verification class at the time of the machine learning. 10. The machine learning apparatus according to claim 9, wherein the class dividing unit divides the pieces of the input/output information such that an amount of pieces of the input/output information contained in each of the classes becomes the same. 11. A method for machine learning using an analytical information storage unit, an analysis-object-set specifying unit and a machine learning unit, wherein the analytical information storage unit stores therein two or more pieces of analytical information each associating a piece of input/output information containing information on input and information on output used for the machine learning with a piece of time-point information indicating a time point of the input/output information, the method comprising: specifying, by the analysis-object-set specifying unit, an analysis object set containing a unit-period input-output set for unit periods divided in a period, the unit-period input-output set being a set of pieces of the input/output information corresponding to pieces of the time-point information indicating the time point in the unit periods and an amount of the pieces of the input/output information of the set being dependent on a period between the time point of the unit periods and a specific time point; and performing, by the machine learning unit, machine learning by using the pieces of the input/output information contained in the analysis object set specified at the specifying. 12. A computer-readable recording medium storing a program for a computer that is enabled to access an analytical information storage unit that stores therein two or more pieces of analytical information each associating a piece of input/output information containing information on input and information on output used for machine learning with a piece of time-point information indicating a time point of the input/output information, the program causing the computer to execute a process comprising: causing the computer to function as an analysis-object-set specifying unit that specifies an analysis object set containing a unit-period input-output set for unit periods divided in a period, the unit-period input-output set being a set of pieces of the input/output information corresponding to pieces of the time-point information indicating the time point in the unit periods and an amount of the pieces of the input/output information of the set being dependent on a period between the time point of the unit periods and a specific time point; and causing the computer to function as a machine learning unit that performs machine learning by using the pieces of the input/output information contained in the analysis object set specified by the analysis-object-set specifying unit.