| CPC G06N 20/20 (2019.01) [G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06N 3/045 (2023.01)] | 20 Claims |
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1. A computer-implemented method, comprising:
accessing a plurality of processing features of a first machine learning model previously trained to process input data of a corpus of input data, wherein the plurality of processing features were utilized by a training framework in training the first machine learning model;
determining a plurality of initial training features according to one or more analyses of input data of the corpus of input data for training a second machine learning model;
combining at least a portion of the plurality of processing features with at least a portion of the plurality of initial training features to form updated training features for training the second machine learning model, the combining comprising:
determining discrete processing features of the plurality of processing features that correspond to discrete training features of the plurality of initial training features; and
for each discrete processing feature having a corresponding discrete training feature:
combining values of the discrete training feature with values of the discrete processing feature to form an updated training feature;
customizing at least some of the updated training features to form customized training features;
incorporating the customized training features into an executable training framework for training the second machine learning model;
initializing the customized training features, wherein initializing includes warm-starting at least one feature of the customized training features from a processing feature of the first machine learning model; and
executing the executable training framework to train the second machine learning model according to at least some input data of the corpus of input data.
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