Normalization source for neural network during application (not training)
2 visualizaciones (últimos 30 días)
Mostrar comentarios más antiguos
Shannon
el 4 de Oct. de 2017
Comentada: Shannon
el 5 de Oct. de 2017
I've built and trained a neural net using z-score normalization of the input parameters. When I now go to apply that network I again apply z-score normalization, but i'm unsure what source to normalize with....the mean and standard deviation of the training data, or the mean and standard deviation of the new data? It seems like a simple question, but I couldn't find a clear answer.
0 comentarios
Respuesta aceptada
Greg Heath
el 5 de Oct. de 2017
A basic assumption is that the design (training and validation) data adequately characterize the summary stationary statistics (e.g., means, variances, maxes, mins and correlations) of all nontraining data (validation, testing and unseen).
The defaults of random datadivision and validation stopping are included to help prevent poor performance on nontraining data.
Now, if new data is encountered whos summary stats vary significantly from the design data, then
a. It could be included in a new design
b. It could be used to obtain an alternate design.
Hope this helps.
Thankyou for formally accepting my answer
Greg
P.S. Don' forget that plotting is a very effective way to recognize nonstationary and incompatible statistics.
Más respuestas (0)
Ver también
Categorías
Más información sobre Pattern Recognition and Classification en Help Center y File Exchange.
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!