  We propose an alternative method for training a classification model. Using
the MNIST set of handwritten digits and Restricted Boltzmann Machines, it is
possible to reach a classification performance competitive to semi-supervised
learning if we first train a model in an unsupervised fashion on unlabeled data
only, and then manually add labels to model samples instead of training data
samples with the help of a GUI. This approach can benefit from the fact that
model samples can be presented to the human labeler in a video-like fashion,
resulting in a higher number of labeled examples. Also, after some initial
training, hard-to-classify examples can be distinguished from easy ones
automatically, saving manual work.
