Accurate ensemble by mutating neural network parameters
AssigneeInternational Business Machines Corporation
InventorHiroshi Inoue
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Abstract
A computer-implemented method is provided for reducing training costs for an ensemble of machine-learning-based classifiers. The method includes training, by a processor, a given machine-learning-based classifier from among the ensemble to obtain a trained classifier. The method further includes dumping, by the processor, parameters used to train the trained classifier to obtain dumped parameters. The method also includes recording, by the processor, change rates of each of the dumped parameters. The method additionally includes creating, by the processor without training, a new classifier from at least one other machine-learning-based classifier in the ensemble by calculating the dumped parameters plus change rates times random numbers for each local prediction by the trained classifier.
Claims20
A computer-implemented method for reducing training costs for an ensemble of machine-learning-based classifiers, the method comprising:
- training, by a processor, a given machine-learning-based classifier from among the ensemble to obtain a trained classifier defined by non-hyper parameters;
- dumping, by the processor, the non-hyper parameters of the trained classifier to obtain dumped parameters;
- recording, by the processor, change rates of each of the dumped parameters;
- and
- mutating, by the processor, the trained classifier to generate a new classifier in the ensemble by, for each local prediction by the trained classifier, combining a random number and the change rates to with the dumped parameters to generate new non-hyper parameters that define the new classifier, wherein the combining generates the new classifier by while avoiding training and its requisite costs to improve computer efficiency.
A computer program product for reducing training costs for an ensemble of machine-learning-based classifiers, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
- training, by a processor of the computer, a given machine-learning-based classifier from among the ensemble to obtain a trained classifier defined by non-hyper parameters;
- dumping, by the processor, the non-hyper parameters of the trained classifier to obtain dumped parameters;
- recording, by the processor, change rates of each of the dumped parameters;
- and
- mutating, by the processor, the trained classifier to generate a new classifier in the ensemble by, for each local prediction by the trained classifier, combining a random number and the change rates with the dumped parameters to generate new non-hyper parameters that define the new classifier, wherein the combining generates the new classifier while avoiding training and its requisite costs to improve computer efficiency.
A computer processing system for reducing training costa for an ensemble of machine-learning-based classifiers, the system comprising:
- a memory for storing program code;
- and
- a hardware processor for running the program code to
- train a given machine-learning-based classifier from among the ensemble to obtain a trained classifier defined by non-hyper parameters;
- dump the non-hyper parameters of the trained classifier to obtain dumped parameters;
- record change rates of each of the dumped parameters;
- and
- mutate the trained classifier to generate a new trained classifier in the ensemble by, for each local prediction by the trained classifier, combining a random number and the change rates with the dumped parameters to generate new non-hyper parameters that define the new classifier, wherein the combining generates the new classifier while avoiding training and its requisite costs to improve computer efficiency.
Dependencies are inferred from the claim text.