Method, apparatus, and system for selecting training observations for machine learning models
AssigneeHERE Global B.V.
InventorsRichard KWANT, Anish MITTAL, David LAWLOR
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Abstract
An approach is provided for selecting training observations for machine learning models. The approach involves determining a first distribution of a plurality of features observed in the training data set, and a second distribution of the plurality of features observed in the candidate pool of observations. The approach further involves selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution. The approach further involves adding the one or more observations to the training data set after annotation. The training data set is used for training the machine learning model.
Claims20
A computer-implemented method for sampling from a candidate pool of observations to create a training data set for a machine learning model comprising:
- determining, by a processor, a first distribution of a plurality of features observed in the training data set;
- determining a second distribution of the plurality of features observed in the candidate pool of observations;
- selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution;
- and
- adding the one or more observations to the training data set after annotation,
- wherein the training data set is used for training the machine learning model.
An apparatus for sampling from a candidate pool of observations to create a training data set for a machine learning model comprising:
- at least one processor;
- and
- at least one memory including computer program code for one or more programs,
- the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,
- determine a first distribution of a plurality of features observed in the training data set;
- determine a second distribution of the plurality of features observed in the candidate pool of observations;
- select one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution;
- and
- add the one or more selected observations to the training data set after annotation,
- wherein the training data set is used for training the machine learning model.
A non-transitory computer-readable storage medium for sampling from a candidate pool of observations to create a training data set for a machine learning model, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:
- determining, by a processor, a first distribution of a plurality of features observed in the training data set;
- determining a second distribution of the plurality of features observed in the candidate pool of observations;
- selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution;
- and
- adding the one or more selected observations to the training data set after annotation,
- wherein the training data set is used for training the machine learning model.
Dependencies are inferred from the claim text.