کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4636909 | 1340730 | 2006 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
VC-dimension and structural risk minimization for the analysis of nonlinear ecological models
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: VC-dimension and structural risk minimization for the analysis of nonlinear ecological models VC-dimension and structural risk minimization for the analysis of nonlinear ecological models](/preview/png/4636909.png)
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 176, Issue 1, 1 May 2006, Pages 166-176
Journal: Applied Mathematics and Computation - Volume 176, Issue 1, 1 May 2006, Pages 166-176
نویسندگان
Giorgio Corani, Marino Gatto,