One-Round Cross-Validation and Uncertainty Determination for Randomized Neural Networks with Applications to Mobile Sensors

Ansgar Steland () and Bart E. Pieters ()
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Ansgar Steland: RWTH Aachen University, Institute of Statistics and AI Center
Bart E. Pieters: Institut für Energie- und Klimaforschung, Forschungszentrum Jülich

A chapter in Artificial Intelligence, Big Data and Data Science in Statistics, 2022, pp 3-24 from Springer

Abstract: Abstract Randomized artificial neural networks such as extreme learning machines provide an attractive and efficient method for supervised learning under limited computing resources and for green machine learning. This especially applies when equipping mobile devices (sensors) with weak artificial intelligence. Results are discussed about supervised learning with such networks and regression methods in terms of consistency and bounds for the generalization and prediction error. Especially, some recent results are reviewed addressing learning with data sampled by moving sensors leading to non-stationary and dependent samples. As randomized networks lead to random out-of-sample performance measures, we study a cross-validation approach to handle the randomness and make use of it to improve out-of-sample performance. Additionally, a computationally efficient approach to determine the resulting uncertainty in terms of a confidence interval for the mean out-of-sample prediction error is discussed based on two-stage estimation. The approach is applied to a prediction problem arising in vehicle integrated photovoltaics.

Keywords: Cross-validation; Extreme learning; Model comparison; Neural network; Photovoltaics; Uncertainty interval (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-07155-3_1

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DOI: 10.1007/978-3-031-07155-3_1

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