Gelß, Patrick and Klus, Stefan and Schuster, Ingmar and Schütte, Christof (2021) Feature space approximation for kernel-based supervised learning. Knowledge-Based Systems, 221 .
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Official URL: https://doi.org/10.1016/j.knosys.2021.106935
Abstract
Abstract We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. Furthermore, the method can be regarded as a regularization technique, which improves the generalizability of learned target functions. We demonstrate significant improvements in comparison to the computation of data-driven predictions involving the full training data set. The method is applied to classification and regression problems from different application areas such as image recognition, system identification, and oceanographic time series analysis.
Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences > Mathematics > Applied Mathematics |
Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics |
ID Code: | 2735 |
Deposited By: | Monika Drueck |
Deposited On: | 15 Feb 2022 17:36 |
Last Modified: | 15 Feb 2022 17:36 |
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