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Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity

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Type
Preprint
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Submission Date
Identifiers
arXiv ID: 1110.0593
Source
arXiv
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Abstract

This thesis derives, tests and applies two linear projection algorithms for machine learning under non-stationarity. The first finds a direction in a linear space upon which a data set is maximally non-stationary. The second aims to robustify two-way classification against non-stationarity. The algorithm is tested on a key application scenario, namely Brain Computer Interfacing.

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