| 摘要 |
A controller of a distributed computing system assigns feature vectors, and assigns data processors and a model updater to first computers. The data processors have charge of iteration calculation of machine learning algorithms, acquire the feature vectors over a network when starting learning, and store the feature vectors in a local storage. In iteration of second and subsequent learning processes, the data processors load the feature vectors from the local storage, and conduct the learning process. The feature vectors are retained in the local storage till completion of learning. The data processors send only the learning results to the model updater, and waits for a next input from the model updater. The model updater conducts the initialization, integration, and convergence check of the model parameters, completes the processing if the model parameters are converged, and transmits new model parameters to the data processor if the model parameters are not converged.
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