Improving biosignal processing through modeling uncertainty: Bayes vs. non-bayes in sleep staging
| العنوان: | Improving biosignal processing through modeling uncertainty: Bayes vs. non-bayes in sleep staging |
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| المؤلفون: | Peter Sykacek, Josef Zeitlhofer, Peter Rappelsberger, Georg Dorffner |
| المصدر: | Applied Artificial Intelligence. 16:395-421 |
| بيانات النشر: | Informa UK Limited, 2002. |
| سنة النشر: | 2002 |
| مصطلحات موضوعية: | Artificial neural network, Computer science, business.industry, Bayesian probability, Pattern recognition, Perceptron, Bayesian inference, Machine learning, computer.software_genre, Bayesian statistics, Bayes' theorem, Artificial Intelligence, Frequentist inference, Artificial intelligence, Biosignal, business, computer |
| الوصف: | In this paper we report about an investigation of Bayesian inference applied to neural networks multilayer perceptrons (MLP), in particular in the task of automatic sleep staging based on electroencephalogram (EEG) and electrooculogram (EOG) signals. The main focus was on evaluating the use of so-called "doubt-levels" and "confidence intervals" ("error bars") in improving the results by rejecting uncertain cases and patterns not well represented by the training set. Bayesian inference is used to arrive at distributions of network weights based on training data. We compare the results of the full-blown Bayesian method with results obtained from a k-nearest neighbor classifier. The results show that the Bayesian technique significantly outperforms the k-nearest-neighbor classifier. At the same time, we show that Bayesian inference, for which we have developed an extension for the calculation of error bars in the latent space of hidden units, can indeed be used for improving results by rejecting cases below ... |
| تدمد: | 1087-6545 0883-9514 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::c8a50430236bc6523ead1f2e932803da https://doi.org/10.1080/08839510290030282 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........c8a50430236bc6523ead1f2e932803da |
| قاعدة البيانات: | OpenAIRE |
PDF full text from Publisher | تدمد: | 10876545 08839514 |
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