System and method for active machine learning

Abstract

An electronic device for active learning includes at least one memory and at least one processor coupled to at least one memory. At least one processor is configured to select one or more entries froma data set including unlabeled data based on a similarity between the one or more entries and labeled data. At least one processor is further configured to cause the one or more entries to be labeled.

Description

technical field [0001] The present disclosure generally relates to machine learning systems. More specifically, the present disclosure relates to systems and methods for active machine learning. Background technique [0002] Active learning (AL) is a method to solve the supervised learning problem without enough labels. Although active learning solutions for classification problems have been proposed, active learning algorithms for sequences are still not widely discussed. As interest in artificial intelligence has grown, many emerging problems have been defined within the context of sequence learning, including image captioning, machine translation, and natural language understanding. In contrast to classification tasks, which require only one label for an example, sequence learning tasks typically require a series of token-level labels for the entire sequence. Precise annotation of sequences is not only labor-intensive, but may also require very specific domain knowledg...

AI Technical Summary

Problems solved by technology

Precise annotation of sequences is not only labor-intensive, but may also require very specific domain knowledge, which is not easily accomplished by crowd-sourcing workers

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