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Abstract:
In many applications, such as credit default prediction and
medical image recognition, test inputs are available in addition to
the labeled training examples. We propose a method to incorporate
the test inputs into learning. Our method results in solutions
having smaller test errors than that of simple training solution,
especially for noisy problems or small training sets.
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