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We propose an alternative method for training a classification model. Usingthe MNIST set of handwritten digits and Restricted Boltzmann Machines, it ispossible to reach a classification performance competitive to semi-supervisedlearning if we first train a model in an unsupervised fashion on unlabeled dataonly, and then manually add labels to model samples instead of training datasamples with the help of a GUI. This approach can benefit from the fact thatmodel samples can be presented to the human labeler in a video-like fashion,resulting in a higher number of labeled examples. Also, after some initialtraining, hard-to-classify examples can be distinguished from easy onesautomatically, saving manual work.

Training a Restricted Boltzmann Machine for Classification by Labeling Model Samples