The Kernel Adaline: A New Algorithm for Non-Linear Signal Processing and Regression
Frieb, Thilo-Thomas and Harrison, R.F.
(1998)
The Kernel Adaline: A New Algorithm for Non-Linear Signal Processing and Regression.
Research Report.
ACSE Research Report 731
.
Department of Automatic Control and Systems Engineering
Abstract
A certain class of non-linear algorithm for signal processing and machine learning is based on the same intrinsic principle. Some given samples (training points) are in the first stage mapped into a very high-dimensional LINEARISATION space (the feature space of pattern recognition theory) and then a linear algorithm performs its work in this space. The expensive expansion into the linearisation space can be performed efficiently using Mercer's kernel functions, as studied by Aizerman et al. in the 1960's. In this work a non-linear adaptation of the (so far linear) Adaline algorithm by Widrow and Hoff is proposed. The new algorithm combines the conceptual simplicity of a least mean square algorithm for linear regression but exhibits the power of a universal non-linear function to approximator. The kernel Adaline algorithm is introduced and the first experimental results are given.
Metadata
Item Type:
Monograph
Authors/Creators:
Frieb, Thilo-Thomas
Harrison, R.F.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)