title: The generalization complexity measure for continuous input data.
abstract: We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, 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.
authors: Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain.
authors: Iván
authors: 0000-0001-7400-7860
authors: I
authors: Gómez
authors: Facultad de Matemática, Astronomía y Física, Universidad Nacional de Córdoba, 5000 Córdoba, Argentina.
authors: Sergio A
authors: SA
authors: Cannas
authors: Facultad de Matemática, Astronomía y Física, Universidad Nacional de Córdoba, 5000 Córdoba, Argentina.
authors: Omar
authors: O
authors: Osenda
authors: Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain.
authors: José M
authors: JM
authors: Jerez
authors: Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain.
authors: Leonardo
authors: 0000-0003-0012-5914
authors: L
authors: Franco
mesh_terms: D000465:Algorithms; D006801:Humans; D008962:Models, Theoretical