Online Semi-supervised Learning for Multi-target Regression in Data Streams Using AMRules

Most data streams systems that use online Multi-target regression yield vast amounts of data which is not targeted. Targeting this data is usually impossible, time consuming and expensive. Semi-supervised algorithms have been proposed to use this untargeted data (input information only) for model improvement. However, most algorithms are adapted to work on batch mode for classification and require huge computational and memory resources.

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