@inbook{8a2ba1bfebbb45ca8d5984221fe3e82b,
title = "Expert mixture methods for adaptive channel equalization",
abstract = "Mixture of expert algorithms are able to achieve a total loss close to the total loss of the best expert over a sequence of examples. We consider the use of mixture of expert algorithms applied to the signal processing problem of channel equalization. We use these mixture of expert algorithms to track the best parameter settings for equalizers in the presence of noise or when the channel characteristics are unknown, maybe non-stationary. The experiments performed demonstrate the use of expert algorithms in tracking the best LMS equalizer step size in the presence of additive noise and in prior selection for the approximate natural gradient (ANG) algorithm.",
author = "Edward Harrington",
year = "2003",
doi = "10.1007/3-540-44989-2_64",
language = "English",
isbn = "3540404082",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "538--545",
editor = "Okyay Kaynak and Ethem Alpaydin and Erkki Oja and Lei Xu",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}