Bagging Technique Using Temporal Expansion Functions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F14%3A86092399" target="_blank" >RIV/61989100:27740/14:86092399 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_39" target="_blank" >http://dx.doi.org/10.1007/978-3-319-08156-4_39</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_39" target="_blank" >10.1007/978-3-319-08156-4_39</a>

Result language
angličtina
Original language name
Bagging Technique Using Temporal Expansion Functions
Original language description
The Bootstrap aggregating (Bagging) technique is widely used in the Machine Learning area, in order to reduce the prediction error of several unstable predictors. The method trains many predictors using bootstrap samples and combine them generating a newpower learning tool. Although, if the training data has temporal dependency the technique is not applicable. One of the most efficient models for the treatment of time series is the Recurrent Neural Network (RNN) model. In this article, we use a RNN toencode the temporal dependency of the input data, then in the new encoding space the Bagging technique can be applied. We analyze the behavior of various neural activation functions for encoding the input data. We use three simulated and three real time-series data to analyze our approach.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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