Impacts of sample design for validation data on the accuracy of feedforward neural network classification

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Impacts of sample design for validation data on the accuracy of feedforward neural network classification

Validation data are often used to evaluate the performance of a trained neural network and used in the selection of a network deemed optimal for the task at-hand. Optimality is commonly assessed with a measure, such as overall classification accuracy. The latter is often calculated directly from a c...

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Bibliographic Details
Main Author:	Foody, Giles
Format:	Article
Published:	MDPI 2017
Subjects:	cross-validation; multi-layer perceptron; remote sensing; classification error; sample design; machine learning
Online Access:	https://eprints.nottingham.ac.uk/45703/

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