| CPC G06F 16/2365 (2019.01) | 19 Claims |
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1. A computer-implemented method, performed in a data processing network node, for acquiring new data samples and for maintaining a set of data samples in a database, wherein the set of data samples are configured to form input to a function associated with a predictive performance, the method comprising
obtaining at least one relevance metric, where the relevance metric is indicative of an increase in the predictive performance of the function when using a data sample as input together with the set of data samples compared to when not using the data sample,
obtaining a relevance criterion, where the relevance criterion identifies relevant data samples in a set of data samples based on the at least one relevance metric,
signaling the relevance criterion to a data collecting network node, and
receiving one or more data samples from the data collecting network node, where the received data samples are associated with relevance metrics that satisfy the relevance criterion; and
training a machine learning model arranged to indicate a novelty metric associated with a data sample in a set of collected data samples, wherein the machine learning model constitutes a relevance metric of the at least one relevance metric.
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