A Classroom Observation Model Fitted to Stochastic and Probabilistic Decision Systems - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2010

A Classroom Observation Model Fitted to Stochastic and Probabilistic Decision Systems

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.
Fichier principal
Vignette du fichier
PoulosBA10.pdf (105.69 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01060648 , version 1 (17-11-2017)

Licence

Attribution

Identifiers

Cite

Marios Poulos, Vassilios S. Belesiotis, Nikolaos Alexandris. A Classroom Observation Model Fitted to Stochastic and Probabilistic Decision Systems. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. pp.30-36, ⟨10.1007/978-3-642-16239-8_7⟩. ⟨hal-01060648⟩
107 View
43 Download

Altmetric

Share

Gmail Facebook X LinkedIn More