Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4636909 | Applied Mathematics and Computation | 2006 | 11 Pages |
Abstract
The problem of distinguishing density-independent (DI) from density-dependent (DD) demographic time series is important for understanding the mechanisms that regulate populations of animals and plants. We address this problem in a novel way by means of Statistical Learning Theory. First, we estimate the VC-dimensions of the best known nonlinear ecological models through the methodology proposed by Vapnik et al. [V. Vapnik, E. Levin, Y. Cun, Measuring the VC-dimension of a learning machine, Neural Comput. 6 (1994) 851-876]. Then, we generate noisy artificial time series, both DI and DD, and use Structural Risk Minimization (SRM) to recognize the model underlying the data from among a suite of alternative candidates. The method shows an encouraging ability in distinguishing between DI and DD time series.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Giorgio Corani, Marino Gatto,