US 12,217,136 B2
Generalized expectation maximization for semi-supervised learning
Felix Schmidt, Niederweningen (CH); Yasha Pushak, Vancouver (CA); and Stuart Wray, Cambridge (GB)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Jul. 22, 2020, as Appl. No. 16/935,313.
Prior Publication US 2022/0027777 A1, Jan. 27, 2022
Int. Cl. G06N 20/00 (2019.01); G06F 16/901 (2019.01); G06N 5/04 (2023.01)
CPC G06N 20/00 (2019.01) [G06F 16/901 (2019.01); G06N 5/04 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A computer-executed method comprising:
assigning a random label to each data point in an unlabeled set of data to produce a working set of data;
partitioning the working set of data into a plurality of data partitions;
producing a respective set of predicted labels corresponding to each data point in the working set of data by, for each given data partition of the plurality of data partitions:
training an interim machine-learning model based on a set of labeled data and the given data partition to produce a trained interim machine-learning model instance corresponding to the given data partition, and
applying the trained interim machine-learning model instance to predict a predicted label for data points in a set of two or more data partitions, of the plurality of data partitions, other than the given data partition,
wherein each predicted label in the respective set of predicted labels of a particular data point in the working set of data is predicted by a respective interim machine-learning model instance trained based on a corresponding data partition, of the plurality of data partitions, other than a particular data partition containing the particular data point;
for each given data point of the working set of data:
based on the set of predicted labels corresponding to the given data point, generating a composite predicted label, and
applying the composite predicted label to the given data point in the working set of data;
after applying the composite predicted label to each data point, of the working set of data, training a particular machine-learning model based, at least in part, on the working set of data and the set of labeled data; and
applying the trained particular machine-learning model to infer a prediction for one or more inference data points;
wherein the method is performed by one or more computing devices.