Nonlinear versus Linear Models in Functional Neuroimaging: Learning Curves and Generalization Crossover
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
We introduce the concept of generalization for models of functional neuroactivation, and show how it is affected by the number, N, of neuroimaging scans available. By plotting generalization as a function of N (i.e. a “learning curve”) we demonstrate that while simple, linear models may generalize better for small N's, more flexible, low-biased nonlinear models, based on artificial neural networks (ANN's), generalize better for larger N's. We demonstrate that for sets of scans of two simple motor tasks—one set acquired with [O15]water using PET, and the other using fMRI—practical N's exist for which “generalization crossover” occurs. This observation supports the application of highly flexible, ANN models to sufficiently large functional activation datasets.
Originalsprog | Engelsk |
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Titel | Information Processing in Medical Imaging : 15th International Conference, IPMI'97 Poultney, Vermont, USA, June 9–13, 1997 Proceedings |
Vol/bind | 1230 |
Forlag | Springer |
Publikationsdato | 1997 |
Sider | 259-270 |
Status | Udgivet - 1997 |
Navn | Lecture Notes in Computer Science |
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Nummer | 1230 |
ISSN | 0302-9743 |
Bibliografisk note
NBI98
Links
- https://link.springer.com/chapter/10.1007/3-540-63046-5_20
Forlagets udgivne version
ID: 259878011