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ISNN
2010
Springer

Extension of the Generalization Complexity Measure to Real Valued Input Data Sets

8 years 10 months ago
Extension of the Generalization Complexity Measure to Real Valued Input Data Sets
Abstract. This paper studies the extension of the Generalization Complexity (GC) measure to real valued input problems. The GC measure, defined in Boolean space, was proposed as a simple tool to estimate the generalization ability that can be obtained when a data set is learnt by a neural network. Using two different discretization methods, the real valued inputs are transformed into binary values, from which the generalization complexity can be straightforwardly computed. The discretization transformation is carried out both through a very simple method based on equal width intervals (EW) and with a more sophisticated supervised method (the CAIM algorithm) that use much more information about the data. A study of the relationship between data complexity and generalization ability obtained was done together with an analysis of the relationship between best neural architecture size and complexity.
Iván Gómez, Leonardo Franco, Jos&eac
Added 28 Jan 2011
Updated 28 Jan 2011
Type Journal
Year 2010
Where ISNN
Authors Iván Gómez, Leonardo Franco, José M. Jerez, José Luis Subirats
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