The Generalization Complexity Measure for Continuous Input Data

IVAN GOMEZ; SERGIO A. CANNAS; OMAR OSENDA; JOSE M. JEREZ; LEONARDO FRANCO

The Scientific World Journal

Hindawi Publishing Corporation

Año: 2014 vol. 2014 p. 815156 - 815156

2356-6140

We introduce in this work an extension for the generalization complexitymeasure to continuous input data.Themeasure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous functions to later on, using an approach based on the use of the set of Walsh functions, consider the case of having a finite number of data points (inputs/outputs pairs), that is, usually the practical case. Using a set of trigonometric functions a model that gives a relationship between the size of the hidden layer of a neural network and the complexity is constructed. Finally, we demonstrate the application of the introduced complexity measure, by using the generated model, to the problem of estimating an adequate neural network architecture for real-world data sets.