Adaptation, Performance and Vapnik-Chervonenkis Dimension of Straight Line Programs

Authors:
José L. Montaña;César L. Alonso;Cruz E. Borges;José L. Crespo
Affiliations:
Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, Santander, Spain 39005;Centro de Inteligencia Artificial, Universidad de Oviedo, Gijón, Spain 33271;Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, Santander, Spain 39005;Departamento de Matemática Aplicada, Estadística y Ciencias de la Computación, Universidad de Cantabria, Santander, Spain 39005
Venue:
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Year:
2009

Citing 7
Cited 1

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Polynomial bounds for VC dimension of sigmoidal and general Pfaffian neural networks

Journal of Computer and System Sciences - Special issue: dedicated to the memory of Paris Kanellakis
Time-space tradeoffs in algebraic complexity theory

Journal of Complexity
Comparison of model selection for regression

Neural Computation
A statistical learning theory approach of bloat

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Straight Line Programs: A New Linear Genetic Programming Approach

ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Linear Genetic Programming

Linear Genetic Programming

Penalty functions for genetic programming algorithms

ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We discuss here empirical comparation between model selection methods based on Linear Genetic Programming. Two statistical methods are compared: model selection based on Empirical Risk Minimization (ERM) and model selection based on Structural Risk Minimization (SRM). For this purpose we have identified the main components which determine the capacity of some linear structures as classifiers showing an upper bound for the Vapnik-Chervonenkis (VC) dimension of classes of programs representing linear code defined by arithmetic computations and sign tests. This upper bound is used to define a fitness based on VC regularization that performs significantly better than the fitness based on empirical risk.