Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of developing a learning model, the method comprising: accessing a sample data set to train the learning model, wherein the learning model comprises a first learning algorithm having a number of inputs and a number of outputs, and wherein each sample of the sample data set comprises a value for each of the inputs and the outputs; determining a number of states for each of the inputs based on the sample data set; selecting a subset of the inputs; partitioning the sample data set into a number of partitions equal to a combined number of states of the selected inputs, wherein each sample of a partition exhibits a state of the selected inputs corresponding to the partition; creating a second learning algorithm for each of the partitions, wherein the second learning algorithm of a corresponding partition comprises logic of the first learning algorithm in which the state of the selected inputs corresponds to the partition, and wherein the second learning algorithm is configured to receive as input those of the inputs that are not the selected inputs; assigning each of the second learning algorithms to one of a plurality of processors of a computing system; training each of the second learning algorithms on the processor assigned to the second learning algorithm using the samples of the partition corresponding to the second learning algorithm; and generating decision logic configured to direct each of a plurality of operational data units as input to one of the second learning algorithms, wherein each of the operational data units comprises a value for each of a plurality of inputs corresponding to the inputs of the sample data set, and wherein the directing of the operational data units is based on a state of the selected inputs corresponding to the operational data unit. 2. The method of claim 1, further comprising: accessing the plurality of operational data units; providing each of the operational data units to the decision logic for execution by a corresponding one of the second learning algorithms on a corresponding one of the processors; and accessing, for each of the operational data units, an output for the operational data unit from the corresponding second learning algorithm. 3. The method of claim 1, wherein: the combined number of states of the selected inputs is a greatest whole number less than or equal to a number of the plurality of processors; and the assigning of each of the second learning algorithms comprises assigning each of the second learning algorithms to a separate one of the plurality of processors. 4. The method of claim 1, wherein: the combined number of states of the selected inputs is a least whole number greater than a number of the plurality of processors of the computing system; and the assigning of each of the second learning algorithms comprises assigning a first one of the second learning algorithms and a second one of the second learning algorithms to a same one of the plurality of processors. 5. The method of claim 4, wherein: the training of each of the second learning algorithms comprises training the first one of the second learning algorithms, followed by training the second one of the second learning algorithms. 6. The method of claim 1, further comprising: determining a value distribution of each of at least some of the inputs; wherein at least one of the selected inputs has a more uniform value distribution than at least one unselected input of the plurality of inputs. 7. The method of claim 1, wherein: the first learning algorithm comprises a first artificial neural network having a first number of hidden neurons; each of the second learning algorithms comprises a second artificial neural network; and the method further comprises setting a second number of hidden neurons of at least one of the second artificial neural networks based on the first number of hidden neurons. 8. The method of claim 7, wherein the second number of hidden neurons is equal to the first number of hidden neurons based on the first number of hidden neurons being less than twice the number of inputs minus twice a number of the selected inputs. 9. The method of claim 7, wherein the second number of hidden neurons is equal to twice the number of inputs minus twice a number of the selected inputs based on the first number of hidden neurons being less than or equal to four times the number of inputs minus four times the number of selected inputs, and being greater than or equal to twice the number of inputs minus twice the number of selected inputs. 10. The method of claim 7, wherein the second number of hidden neurons is equal to the first number of hidden neurons minus twice the number of inputs, minus twice a number of the selected inputs, based on the first number of hidden neurons being greater than four times the number of inputs minus four times the number of selected inputs. 11. The method of claim 1, wherein the determining of the number of states for each of the inputs based on the sample data set comprises determining a number of possible states for each of the selected inputs of the sample data set. 12. The method of claim 2, wherein the determining of the number of states for each of the inputs based on the sample data set comprises determining a number of employed states for each of the selected inputs of the sample data set. 13. The method of claim 12, wherein: the accessing of the operational data units comprises accessing a first operational data unit that includes a first input of the selected inputs having a state that is not employed in the sample data set; and the decision logic is further configured to direct the first operational data unit as input to the second learning algorithm corresponding to one of the employed states of the first input. 14. The method of claim 12, wherein: the accessing of the operational data units comprises accessing a first operational data unit that includes a first input of the selected inputs having a state that is not employed in the sample data set; the decision logic is further configured to direct the first operational data unit as input to at least two of the second learning algorithms, wherein each of the at least two of the second learning algorithms corresponds to one of the employed states of the first input; and the method further comprises calculating a weighted average of corresponding outputs of the at least two of the second learning algorithms to produce an output for the first operational data unit. 15. The method of claim 1, wherein the first learning algorithm and each of the second learning algorithms comprises an artificial neural network. 16. The method of claim 1, wherein the first learning algorithm and each of the second learning algorithms comprises a supervised learning algorithm. 17. The method of claim 1, wherein the first learning algorithm and each of the second learning algorithms comprises an unsupervised learning algorithm. 18. A non-transitory computer-readable storage medium comprising instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations comprising: accessing a sample data set to train a learning model, wherein the learning model comprises a first learning algorithm having a number of inputs and a number of outputs, and wherein each sample of the sample data set comprises a value for each of the inputs and the outputs; determining a number of states for each of the inputs based on the sample data set; selecting a subset of the inputs; partitioning the sample data set into a number of partitions equal to a combined number of states of the selected inputs, wherein each sample of a partition exhibits a state of the selected inputs corresponding to the partition; creating a second learning algorithm for each of the partitions, wherein the second learning algorithm of a corresponding partition comprises logic of the first learning algorithm in which the state of the selected inputs corresponds to the partition, and wherein the second learning algorithm is configured to receive as input those of the inputs that are not the selected inputs; assigning each of the second learning algorithms to one of a plurality of processors of a computing system; training each of the second learning algorithms on the processor assigned to the second learning algorithm using the samples of the partition corresponding to the second learning algorithm; and generating decision logic configured to direct each of a plurality of operational data units as input to one of the second learning algorithms, wherein each of the operational data units comprises a value for each of a plurality of inputs corresponding to the inputs, and wherein the directing of the operational data units is based on the state of the selected inputs corresponding to the operational data unit. 19. A computing system comprising: a sample data set accessing module configured to access a sample data set to train a learning model, wherein the learning model comprises a first learning algorithm having a number of inputs and a number of outputs, and wherein each sample of the sample data set comprises a value for each of the inputs and the outputs; a sample data set partitioning module configured to determine a number of states for each of the inputs based on the sample data set, select a subset of the inputs, and partition the sample data set into a number of partitions equal to a combined number of states of the selected inputs, wherein each sample of a partition exhibits a state of the selected inputs corresponding to the partition; an algorithm creation module configured to create a second learning algorithm for each of the partitions, wherein the second learning algorithm of a corresponding partition comprises logic of the first learning algorithm in which the state of the selected inputs corresponds to the partition, and wherein the second learning algorithm is configured to receive as input those of the inputs that are not the selected inputs; an algorithm assignment module configured to assign each of the second learning algorithms to one of a plurality of processors of the computing system; an algorithm training module configured to train each of the second learning algorithms on the processor assigned to the second learning algorithm using the samples of the partition corresponding to the second learning algorithm; and a decision logic generation module configured to generate decision logic configured to direct each of a plurality of operational data units as input to one of the second learning algorithms, wherein each of the operational data units comprises a value for each of a plurality of inputs corresponding to the inputs of the sample data set, and wherein the directing of the operational data units is based on the state of the selected inputs corresponding to the operational data unit. 20. The system of claim 19, further comprising: an operational data unit accessing module configured to access the plurality of operational data units, and provide each of the operational data units to the decision logic for execution by a corresponding one of the second learning algorithms on a corresponding one of the processors; and an operational output accessing module configured to access, for each of the operational data units, an output for the operational data unit from the corresponding second learning algorithm.