Abstract
This paper focuses on solving the problems of preparing and normalizing data that are captured from a classroom observation, and are linked with significant relevant properties. We adapt these data using a Bayesian model that creates normalization conditions to a well fitted artificial neural network. We separate the method in two stages: first implementing the data variable in a functional multi-factorial normalization analysis using a normalizing constant and then using constructed vectors containing normalization values in the learning and testing stages of the selected learning vector quantifier neural network.
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Poulos, M., Belesiotis, V.S., Alexandris, N. (2010). A Classroom Observation Model Fitted to Stochastic and Probabilistic Decision Systems. In: Papadopoulos, H., Andreou, A.S., Bramer, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2010. IFIP Advances in Information and Communication Technology, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16239-8_7
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DOI: https://doi.org/10.1007/978-3-642-16239-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16238-1
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