Weight generation in machine learning
AssigneeCalifornia Institute of Technology
InventorsYaser Said Abu-Mostafa, Carlos Roberto Gonzalez
Life of this patent
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Abstract
Technologies are generally described for systems, devices and methods relating to a machine learning environment. In some examples, a processor may identify a training distribution of a training data. The processor may identify information about a test distribution of a test data. The processor may identify a coordinate of the training data and the test data. The processor may determine, for the coordinate, differences between the test distribution and the training distribution. The processor may determine weights based on the differences. The weights may be adapted to cause the training distribution to conform to the test distribution when the weights are applied to the training distribution.
Claims23
A method to improve predictive capability of a machine learning system, the method comprising:
- obtaining, by a processor of a computer, training data stored in a memory;
- identifying, by the processor, a training distribution of the training data;
- obtaining, by the processor, test data stored in the memory;
- identifying, by the processor, information about a test distribution of the test data;
- identifying, by the processor, coordinates of the training data and the test data;
- determining, by the processor, for each identified coordinate, differences between the test data and the training data;
- determining, by the processor, weights based on the determined differences, wherein the weights are adapted to cause the training distribution to match to the test distribution in response to the weights being applied to the training distribution;
- generating, by the processor, a function based on the training distribution and the weights;
- and
- generating labels, in response to an application of the test data to the function,
- wherein generating labels in response to the application of the test data to the function facilitates matching of the test distribution and the training distribution to improve a predictive capability of the machine learning system.
A method to improve predictive capability of a machine learning system, the method comprising, by a computer:
- identifying a training distribution of first points of training data;
- identifying information about a test distribution of second points of test data;
- determining a first frequency for the training distribution of the first points;
- determining a second frequency for the test distribution of the second points;
- comparing the first frequency and the second frequency;
- calculating a weight based on differences between the first frequency and the second frequency;
- generating a weighted function based on the training distribution and the calculated weight;
- generating labels, in response to an application of the test data to the weighted function;
- wherein the weighted function facilitates a match of a particular point in the training distribution to a particular point in the test distribution, and wherein the match improves the predictive capability of the machine learning system.
A computing device, comprising:
- a first processor;
- a second processor;
- and
- a memory configured to be in communication with the first processor and the second processor,
- wherein the memory is effective to store training data and test data,
- wherein the first processor is effective to:
- retrieve training data stored in the memory;
- identify a training distribution of the training data;
- retrieve test data from the memory;
- identify information about a test distribution of the test data;
- identify one or more coordinates of the training data and the test data
- determine a first frequency, wherein the first frequency relates to the training data;
- determine a second frequency, wherein the second frequency relates to the test data;
- compare the first frequency and the second frequency;
- and
- determine a weight for the training data, wherein the determination of the weight is based at least, in part, on the comparison of the first frequency and the second frequency, and
- wherein the second processor is effective to:
- generate a weighted function based on the determined weight and the training data;
- and
- operate the computing device to generate labels, in response to an application of the test data to the weighted function, wherein the generation of the labels in response to the application of the test data to the weighted function facilitates a match of the test distribution and the training distribution to improve a predictive capability of the computing device.
Dependencies are inferred from the claim text.