A dynamic time-evolution Boltzmann machine capable of learning is provided. Aspects include acquiring a time-series input data and supplying a plurality of input values of input data of the time-series input data at one time point to a plurality of nodes of the mode. Aspects also include computing, based on an input data sequence before the one time point in the time-series input data and a weight parameter between each of a plurality of input values of input data of the input data sequence and a corresponding one of the plurality of nodes of the model, a conditional probability of the input value at the one time point given that the input data sequence has occurred. Aspects further include adjusting the weight parameter so as to increase a conditional probability of occurrence of the input data at the one time point given that the input data sequence has occurred.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning apparatus for learning a model corresponding to time-series input data, comprising: an acquiring unit that acquires the time-series input data; a supplying unit that supplies a plurality of input values of input data of the time-series input data at one time point to a plurality of nodes of the model; a computing unit that computes, on the basis of an input data sequence before the one time point in the time-series input data and a weight parameter between each of a plurality of input values of input data of the input data sequence and a corresponding one of the plurality of nodes of the model, a conditional probability of the input value at the one time point given that the input data sequence has occurred; and a learning processing unit that adjusts the weight parameter so as to increase a conditional probability of occurrence of the input data at the one time point given that the input data sequence has occurred. 2. The learning apparatus according to claim 1, further comprising: an updating unit that updates a plurality of update parameters that are based on the input data sequence before the one time point in the time-series input data, from values for a prior time point to values for the one time point on the basis of values of the plurality of update parameters and input values of input data to be reflected next, wherein the computing unit computes, using the plurality of update parameters, a conditional probability of an input data value at the one time point given that the input data sequence has occurred. 3. The learning apparatus according to claim 2, wherein the weight parameter is based on a learning parameter to be learned and a predefined parameter that changes in a predetermined manner in accordance with a time difference between the input data of the input data sequence and the input data at the one time point. 4. The learning apparatus according to claim 3, wherein each of the plurality of update parameters is based on an input value of input data at each time point in the input data sequence and the predefined parameter of the weight parameter between the input value and the corresponding node. 5. The learning apparatus according to claim 4, wherein the predefined parameter is based on a value obtained by raising a predetermined constant to the power of a value based on the time difference, and the updating unit updates each of the plurality of update parameters by adding a corresponding input value of input data to be reflected next to the update parameter for the prior time point before the one time point and then multiplying the resulting sum by the predetermined constant. 6. The learning apparatus according to claim 5, wherein the weight parameter is based on a positive value, which is based on a product of a first learning parameter and a first predefined parameter, and a negative value, which is based on a second learning parameter and a second predefined parameter. 7. The learning apparatus according to claim 6, wherein the weight parameter takes a positive value that is based on a product of the first learning parameter and the first predefined parameter in a case where the time difference is greater than or equal to a predetermined delay constant and takes a negative value that is based on a product of the second learning parameter and the second predefined parameter in a case where the time difference is less than the predetermined delay constant and is not equal to 0. 8. The learning apparatus according to claim 6, wherein the weight parameter is based on a plurality of positive values, which are based on products of a plurality of sets of the first learning parameter and the first predefined parameter respectively from among a plurality of the first learning parameters and a plurality of the first predefined parameters, and a plurality of negative values, which are based on products of a plurality of sets of the second learning parameter and the second predefined parameter respectively from among a plurality of the second learning parameters and a plurality of the second predefined parameters. 9. The learning apparatus according to claim 6, wherein the first predefined parameter takes a value obtained by raising a first constant to the power of a value obtained by subtracting the predetermined delay constant from the time difference, the first constant being greater than 0 and less than 1, and the second predefined parameter takes a value obtained by raising a second constant to the power of a negative value of the time difference, the second constant being greater than 0 and less than 1. 10. The learning apparatus according to claim 5, further comprising: a FIFO memory that sequentially stores a corresponding input value of new input data of the time-series input data and outputs the input value after a predetermined number of time points, wherein the updating unit updates the plurality of update parameters by using the input value output from the FIFO memory. 11. The learning apparatus according to claim 10, wherein a plurality of the FIFO memories are each provided between a corresponding one of the plurality of input values of past input data in the time-series input data and a corresponding one of the plurality of nodes. 12. The learning apparatus according to claim 1, wherein the acquiring unit sequentially acquires new input data of the time-series input data, the supplying unit sequentially supplies a plurality of input data values of the new input data to the plurality of nodes, the computing unit computes a conditional probability of an input data value at a time point corresponding to the new input data given that an input data sequence preceding the new input data in the time-series input data has occurred, and the learning processing unit adjusts the weight parameter so as to increase a conditional probability of occurrence of the new input data given that the input data sequence has occurred. 13. The learning apparatus according to claim 1, wherein the learning processing unit adjusts the weight parameter collectively for a plurality of time points in response to acquisition of new input data at the plurality of time points. 14. The learning apparatus according to claim 1, wherein the learning processing unit further adjusts a bias parameter given to each of the plurality of nodes of the model. 15. A processing apparatus that predicts next input data from time-series input data by using a model corresponding to the time-series input data, comprising: a time-series data acquiring unit that acquires the time-series input data; a supplying unit that supplies a plurality of input values corresponding to a prediction target time point to a plurality of nodes of the model; a node computing unit that computes, on the basis of an input data sequence before the prediction target time point in the time-series input data and a weight parameter between each of a plurality of input values of input data in the input data sequence and a corresponding one of the plurality of nodes of the model, a conditional probability of the input value corresponding to the prediction target time point given that the input data sequence has occurred; and a data computing unit that computes, on the basis of the conditional probabilities of the input values corresponding to the prediction target time point, a conditional probability of next input data being a predetermined value given that the time-series input data has occurred. 16. The processing apparatus according to claim 15, wherein the model does not have a weight parameter between the plurality of nodes, and the data computing unit computes, as the conditional probability of the next input data being the predetermined value given that the time-series input data has occurred, a product of conditional probabilities of the plurality of nodes having a value corresponding to the predetermined value at the prediction target time point that are determined for the plurality of nodes given that the time-series input data has occurred. 17.-20. (canceled)