Functional support vector machines and generalized linear models for glacier geomorphology analysis

Abstract

We propose a functional pattern recognition approach to the problem of identifying the topographic profiles of glacial and fluvial valleys, using a functional version of support vector machines (SVMs) for classification. We compare a proposed functional version of SVMs with functional generalized linear models and their vectorial versions: generalized linear models and SVMs that use the original observations as input. The results indicate the benefit of our proposed functional SVMs and, in more general terms, the advantages of using a functional rather than a vectorial approach.

Keywords:

• digital elevation models
• functional data analysis
• functional general lineal model
• support vector machines
• topographic profiles

2000 AMS subject Classification :

• 62J12
• 62J99
• 68T05
• 68T35
• 86A40

Acknowledgements

J.M. Matías's research was supported by the Spanish Ministry of Education and Science, Grant No. MTM2005-00820.