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

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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.

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