Repository: Freie Universität Berlin, Math Department

Feature space approximation for kernel-based supervised learning

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
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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