While statistics focusses on hypothesis testing and on estimating (properties
of) the true sampling distribution, in machine learning the performance of
learning algorithms on future data is the primary issue. In this paper we bridge
the gap with a general principle (PHI) that identifies hypotheses with best
predictive performance. This includes predictive point and interval estimation,
simple and composite hypothesis testing, (mixture) model selection, and
others as special cases. For...[Show more]
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