title: Neural architecture design based on extreme learning machine.
abstract: Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages.
authors.affiliation: Dpto. Informática de Sistemas, Universidad Católica San Antonio, Murcia, Spain. Electronic address: abueno@ucam.edu.
authors.forename: Andrés
authors.initials: A
authors.lastname: Bueno-Crespo
authors.forename: Pedro J
authors.initials: PJ
authors.lastname: García-Laencina
authors.forename: José-Luis
authors.initials: JL
authors.lastname: Sancho-Gómez
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003199:Computer Systems; D003627:Data Interpretation, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D015203:Reproducibility of Results
keywords: Architecture design; Extreme learning machine; Multilayer perceptron; Neural networks