@inproceedings{99159673581542dd94d3e4de1b90667c,
title = "Adaptive polynomial filters with individual learning rates for computationally efficient lung tumor motion prediction",
abstract = "This paper presents a study of higher-order neural units as polynomial adaptive filters with multiple-learning-rate gradient descent for 3-D lung tumor motion prediction. The method is compared with single-learning rate gradient descent approaches with and without learning rate normalization. Experimental analysis is done with linear and quadratic neural unit. The influence of correct selection of adaptation parameters and the dependence of learning time on accuracy were experimentally analyzed. The prediction accuracy is nearly equal to recently published results of batch retraining approaches while the computational efficiency is higher for the introduced approach.",
keywords = "Gradient Descent, Linear Neural Unit, Prediction, Quadratic Neural Unit",
author = "Matous Cejnek and Ivo Bukovsky and Noriyasu Homma and Ond{\v r}ej L{\'i}{\v s}ka",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2015 ; Conference date: 29-10-2015 Through 30-10-2015",
year = "2015",
month = dec,
day = "3",
doi = "10.1109/IWCIM.2015.7347077",
language = "English",
series = "2015 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2015",
address = "United States",
}