L. J. Fitzgibbon, L. Allison & D. L. Dowe

Proc. 11th Int. Workshop on Algorithmic Learning Theory (ALT2000), H. Arimura, S. Jain & A. Sharma (eds), Sydney, pp.56-70, LNCS #1968, doi:10.1007/3-540-40992-0_5, 2000

Summary: Given a series of multivariate data, approximate it by a piece-wise constant function. How many cut-points are there? What are the means and variances of the segments? Where should the cut points be placed? The simplest model is a single segment. The most complex model has one segment per data point. The best model is generally somewhere between these extremes. Only by considering model complexity can a reasonable inference be made.

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