Technical Reports

1994

@techreport{HLL1994,
	vgclass =	{report},
	vgproject =	{nn},
	author =	{L.K. Hansen and B. Lautrup and I. Law and N. M{\o}rch and
	J. Thomsen},
	title =	{Extremely Ill-posed Learning},
	institution =	{CONNECT},
	month =	{August},
	year =	{1994},
	abstract =	{Extremely ill-posed learning problems are common in image
	and spectral analysis. They are characterised by a vast number of
	highly correlated inputs, e.g. pixel or pin values, and a modest number
	of patterns, e.g. images or spectra. We show that it is possible to
	train neural networks to learn such patterns without using an excessive
	number of weights, and we devise a test to decide if new patterns
	should be included in the training set or whether they fall within the
	subspace already explored. The method is applied to the analysis of
	PET-images.},
}