Patent ID: 11893473
Assignee: EMC IP HOLDING COMPANY LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A computer program product which is tangibly stored on a non-transitory computer readable medium and comprises machine-executable instructions which, when executed by a device, causing the device to:
process first input data by using a first machine learning model having first parameter set values, to obtain first feature information of the first input data, the first machine learning model having a capability of self-ordering and the first parameter set values being updated after the processing of the first input data;
generate a first classification result for the first input data based on the first feature information by using a second machine learning model having second parameter set values;
process second input data by using the first machine learning model having the updated first parameter set values, to obtain second feature information of the second input data;
generate a second classification result for the second input data based on the second feature information by using the second machine learning model having the second parameter set values; and
generate adaptive change output data based on changes in the first input data and the second input data;
wherein the machine-executable instructions, when executed by the device, further cause the device to:
store at least one of the first feature information and the second feature information; and
in accordance with a presence of at least one of a first ground-truth classification result for the first input data and a second ground-truth classification result for the second input data, and in accordance with a determination that an update of the second machine learning model is triggered, re-train a duplicated model of the second machine learning model by using at least one of a pair of the first feature information and the first ground-truth classification result;
wherein the first machine learning model comprises an unsupervised spiking neural network; and
wherein the second machine learning model comprises a machine learning model other than an unsupervised spiking neural network and a supervised spiking neural network.