Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity
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 algo...
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| creator | Blythe, Duncan A. J |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.1110.0593 |
| format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.1110.0593</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Learning |
| title | Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity |
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