Active learning makes it possible to use both labeled and unlabeled data to train a classifier. This video describes how the Wolfram Language functions ActiveClassification and ActivePrediction can be used to build models by actively sampling a configuration space. Also included is a section on SequencePredict, the function that can be used to learn from existing sequences to predict which element should occur next in the sequence.
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You'll Learn To
- Build classifiers and predictors by actively sampling unlabeled data
- Work with the ActiveClassification and ActivePrediction functions
- Train a predictor on a set of sequences to generate new samples