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A method, system, and computer program product provides automated determination of the size of the sample that is to be used in training a neural network data mining model that is large enough to properly train the neural network data mining model, yet is no larger than is necessary. A method of per

A method, system, and computer program product provides automated determination of the size of the sample that is to be used in training a neural network data mining model that is large enough to properly train the neural network data mining model, yet is no larger than is necessary. A method of performing training of a neural network data mining model comprises the steps of: a) providing a training dataset for training an untrained neural network data mining model, the first training dataset comprising a plurality of rows of data, b) selecting a row of data from the training dataset for performing training processing on the neural network data mining model, c) computing an estimate of a gradient or cost function of the neural network data mining model, d) determining whether the gradient or cost function of the neural network data mining model has converged, based on the computed estimate of the gradient or cost function of the neural network data mining model, e) repeating steps b)-d), if the gradient or cost function of the neural network data mining model has not converged, and f) updating weights of the neural network data mining model, if the gradient or cost function of the neural network data mining model has converged.

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What is claimed is: 1. A computer implemented method of performing training of a neural network data mining model comprising: a) providing a training dataset to a neural network for training an untrained neural network data mining model, the training dataset comprising a plurality of rows of data,

What is claimed is: 1. A computer implemented method of performing training of a neural network data mining model comprising: a) providing a training dataset to a neural network for training an untrained neural network data mining model, the training dataset comprising a plurality of rows of data, wherein the neural network data mining model is trained by: b) selecting a row of data from the training dataset for performing training processing on the neural network data mining model; c) computing an estimate of a gradient or cost function of the neural network data mining model based on the selected row of data, wherein the cost function accounts for the cost of various nodal transitions; d) determining whether the gradient or cost function of the neural network data mining model has converged using the selected row of data, based on the computed estimate of the gradient or cost function of the neural network data mining model, wherein the computing an estimate is performed before the determining; e) repeating steps b)-d) using another row of data, if the gradient or cost function of the neural network data mining model has not converged; f) updating weights of the neural network data mining model if the gradient or cost function of the neural network data mining model has converged and providing the number of rows of data that were used for performing training processing on the neural network data mining model, wherein the number of rows that were used is a subset of the total number of rows in the training dataset, and wherein the neural network data mining model uses less than the entire training dataset to train the neural network data mining model; and g) performing additional training processing to the neural network data mining model using the training dataset, wherein the additional training processing is performed using a number of rows of data equal to the provided number of rows of data. 2. The method of claim 1, wherein the selecting step comprises the step of: randomly selecting the row of data from the training dataset for performing training processing on the neural network data mining model. 3. The method of claim 1, further comprising the step of: performing additional training processing to the neural network data mining model by repeating steps b)-f) until a decrease in an error of the neural network data mining model is less than a predefined threshold. 4. A computer implemented method of performing training of a neural network data mining model comprising: a) providing a training dataset to a neural network for training an untrained neural network data mining model, the training dataset comprising a plurality of rows of data, wherein the neural network data mining model is trained by: b) selecting, in each of a plurality of threads, a row of data from the training dataset for performing training processing on the neural network data mining model; c) computing, in each of the plurality of threads, an estimate of a gradient or cost function of the neural network data mining model based on the selected row of data, wherein the cost function accounts for the cost of various nodal transitions; d) consolidating the computed estimate of the gradient or cost function of the neural network data mining model from each thread based on the selected row of data to form a consolidated gradient or cost function; e) determining whether the consolidated gradient or cost function of the neural network data mining model has converged using the selected row of data, based on the consolidated computed estimate of the gradient or cost function of the neural network data mining model, wherein the computing an estimate is performed before the determining; f) repeating steps b)-d) using another row of data, if the consolidated gradient or cost function of the neural network data mining model has not converged; and g) updating weights of the neural network data mining model if the consolidated gradient or cost function of the neural network data mining model has converged and providing the number of rows of data that were used for performing training processing on the neural network data mining model for each thread in the plurality of threads and consolidating the number of rows for each thread, wherein the consolidated number of rows is a subset of the total number of rows in the training dataset, and wherein the neural network data mining model uses less than the entire training dataset to train the neural network data mining model; and h) performing additional training processing to the neural network data mining model using the training dataset, wherein the additional training processing is performed using a number of rows of data equal to the consolidated number of rows. 5. The method of claim 4, wherein the selecting step comprises the step of: randomly selecting, in each of the plurality of threads, the row of data from the training dataset for performing training processing on the neural network data mining model. 6. The method of claim 4, wherein each thread processes an equal portion of the number of rows of data processed. 7. The method of claim 4, further comprising the step of: performing additional training processing to the neural network data mining model by repeating steps b)-f) until a decrease in an error of the neural network data mining model is less than a predefined threshold. 8. A computer implemented system for performing training of a neural network data mining model, comprising: a processor operable to execute computer program instructions; and a memory operable to store computer program instructions executable by the processor, the computer program instructions for performing the steps of: a) providing a training dataset to a neural network for training an untrained neural network data mining model, the training dataset comprising a plurality of rows of data, wherein the neural network data mining model is trained by: b) selecting a row of data from the training dataset for performing training processing on the neural network data mining model; c) computing an estimate of a gradient or cost function of the neural network data mining model based on the selected row of data, wherein the cost function accounts for the cost of various nodal transitions; d) determining whether the gradient or cost function of the neural network data mining model has converged using the selected row of data, based on the computed estimate of the gradient or cost function of the neural network data mining model, wherein the computing an estimate is performed before the determining; e) repeating steps b)-d) using another row of data, if the gradient or cost function of the neural network data mining model has not converged; and f) updating weights of the neural network data mining model if the gradient or cost function of the neural network data mining model has converged and providing the number of rows of data that were used for performing training processing on the neural network data mining model, wherein the number of rows that were used is a subset of the total number of rows in the training dataset and wherein the neural network data mining model uses less than the entire training dataset to train the neural network data mining model; and g) performing additional training processing to the neural network data mining model using the training dataset, wherein the additional training processing is performed using a number of rows of data equal to the provided number of rows of data. 9. The system of claim 8, wherein the selecting step comprises the step of: randomly selecting the row of data from the training dataset for performing training processing on the neural network data mining model. 10. The method of claim 8, further comprising the step of: performing additional training processing to the neural network data mining model by repeating steps b)-f) until a decrease in an error of the neural network data mining model is less than a predefined threshold. 11. A computer implemented system for performing training of a neural network data mining model, comprising: a processor operable to execute computer program instructions; and a memory operable to store computer program instructions executable by the processor in a multi-threaded environment, the computer program instructions for performing the steps of: a) providing a training dataset to a neural network for training an untrained neural network data mining model, the training dataset comprising a plurality of rows of data, wherein the neural network data mining model is trained by: b) selecting, in each of a plurality of threads, a row of data from the training dataset for performing training processing on the neural network data mining model; c) computing, in each of the plurality of threads, an estimate of a gradient or cost function of the neural network data mining model based on the selected row of data, wherein the cost function accounts for the cost of various nodal transitions; d) consolidating the computed estimate of the gradient or cost function of the neural network data mining model from each thread based on the selected row of data to form a consolidated gradient or cost function; e) determining whether the consolidated gradient or cost function of the neural network data mining model has converged using the selected row of data, based on the consolidated computed estimate of the gradient or cost function of the neural network data mining mode, wherein the computing an estimate is performed before the determining; f) repeating steps b)-e) using another row of data, if the consolidated gradient or cost function of the neural network data mining model has not converged; and g) updating weights of the neural network data mining model if the consolidated gradient or cost function of the neural network data mining model has converged and providing the number of rows of data that were used for performing training processing on the neural network data mining model for each thread in the plurality of threads and consolidating the number of rows for each thread, wherein the consolidated number of rows is a subset of the total number of rows in the training dataset, and wherein the neural network data mining model uses less than the entire training dataset to train the neural network data mining model; and h) performing additional training processing to the neural network data mining model using the training dataset, wherein the additional training processing is performed using a number of rows of data equal to the consolidated number of rows. 12. The system of claim 11, wherein the selecting step comprises the step of: randomly selecting, in each of the plurality of threads, the row of data from the training dataset for performing training processing on the neural network data mining model. 13. The method of claim 11, wherein each thread processes an equal portion of the number of rows of data processed. 14. The system of claim 11, further comprising the step of: performing additional training processing to the neural network data mining model by repeating steps b)-f) until a decrease in an error of the neural network data mining model is less than a predefined threshold. 15. A computer program product for performing training of a neural network data mining model in an electronic data processing computer program product, comprising: a computer readable medium; computer program instructions, recorded on the computer readable medium, executable by a processor, for performing the steps of: a) providing a training dataset to a neural network for training an untrained neural network data mining model, the training dataset comprising a plurality of rows of data, wherein the neural network data mining model is trained by: b) selecting a row of data from the training dataset for performing training processing on the neural network data mining model; c) computing an estimate of a gradient or cost function of the neural network data mining model based on the selected row of data, wherein the cost function accounts for the cost of various nodal transitions; d) determining whether the gradient or cost function of the neural network data mining model has converged using the selected row of data, based on the computed estimate of the gradient or cost function of the neural network data mining model, wherein the computing an estimate is performed before the determining; e) repeating steps b)-d) using another row of data, if the gradient or cost function of the neural network data mining model has not converged; and f) updating weights of the neural network data mining model if the gradient or cost function of the neural network data mining model has converged and providing the number of rows of data that were used for performing training processing on the neural network data mining model, wherein the number of rows that were used is a subset of the total number of rows in the training dataset, wherein the neural network data mining model uses less than the entire training dataset to train the neural network data mining model; and h) performing additional training processing to the neural network data mining model using the training dataset, wherein the additional training processing is performed using a number of rows of data equal to the provided number of rows of data. 16. The computer program product of claim 15, wherein the selecting step comprises the step of: randomly selecting the row of data from the training dataset for performing training processing on the neural network data mining model. 17. The computer program product of claim 15, further comprising the step of: performing additional training processing to the neural network data mining model by repeating steps b)-f) until a decrease in an error of the neural network data mining model is less than a predefined threshold. 18. A computer program product for performing training of a neural network data mining model in a multi-threaded environment in an electronic data processing system, comprising: a computer readable medium; computer program instructions, recorded on the computer readable medium, executable by a processor, for performing the steps of: a) providing a training dataset to a neural network for training an untrained neural network data mining model, the training dataset comprising a plurality of rows of data, wherein the neural network data mining model is trained by: b) selecting, in each of a plurality of threads, a row of data from the training dataset for performing training processing on the neural network data mining model; c) computing, in each of the plurality of threads, an estimate of a gradient or cost function of the neural network data mining model based on the selected row of data, wherein the cost function accounts for the cost of various nodal transitions; d) consolidating the computed estimate of the gradient or cost function of the neural network data mining model from each thread based on the selected row of data to form a consolidated gradient or cost function; e) determining whether the consolidated gradient or cost function of the neural network data mining model has converged using the selected row of data, based on the consolidated computed estimate of the gradient or cost function of the neural network data mining model, wherein the computing an estimate is performed before the determining; f) repeating steps b)-e) using another row of data, if the consolidated gradient or cost function of the neural network data mining model has not converged; and g) updating weights of the neural network data mining model if the consolidated gradient or cost function of the neural network data mining model has converged and providing the number of rows of data that were used for performing training processing on the neural network data mining model for each thread in the plurality of threads and consolidating the number of rows for each thread, wherein the consolidated number of rows is a subset of the total number of rows in the training dataset, and wherein the neural network data mining model uses less than the entire training dataset to train the neural network data mining model; and h) performing additional training processing to the neural network data mining model using the training dataset, wherein the additional training processing is performed using a number of rows of data equal to the consolidated number of rows. 19. The computer program product of claim 18, wherein the selecting step comprises the step of: randomly selecting, in each of the plurality of threads, the row of data from the training dataset for performing training processing on the neural network data mining model. 20. The computer program product of claim 19, wherein each thread processes an equal portion of the number of rows of data processed. 21. The computer program product of claim 20, further comprising the step of: performing additional training processing to the neural network data mining model by repeating steps b)-f) until a decrease in an error of the neural network data mining model is less than a predefined threshold.