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Broad Learning for Optimal Short-Term Traffic Flow Prediction. / Liu, Di; Yu, Wenwu; Baldi, Simone.

Advances in Neural Networks : Proceedings 16th International Symposium on Neural Networks (ISNN 2019). ed. / Huchuan Lu; Huajin Tang; Zhanshan Wang. Cham, Switzerland : Springer, 2019. p. 232-239 (Lecture Notes in Computer Science (LNCS); Vol. 11554).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Liu, D, Yu, W & Baldi, S 2019, Broad Learning for Optimal Short-Term Traffic Flow Prediction. in H Lu, H Tang & Z Wang (eds), Advances in Neural Networks : Proceedings 16th International Symposium on Neural Networks (ISNN 2019). Lecture Notes in Computer Science (LNCS), vol. 11554, Springer, Cham, Switzerland, pp. 232-239, 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russian Federation, 10/07/19. https://doi.org/10.1007/978-3-030-22796-8_25

APA

Liu, D., Yu, W., & Baldi, S. (2019). Broad Learning for Optimal Short-Term Traffic Flow Prediction. In H. Lu, H. Tang, & Z. Wang (Eds.), Advances in Neural Networks : Proceedings 16th International Symposium on Neural Networks (ISNN 2019) (pp. 232-239). (Lecture Notes in Computer Science (LNCS); Vol. 11554). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-22796-8_25

Vancouver

Liu D, Yu W, Baldi S. Broad Learning for Optimal Short-Term Traffic Flow Prediction. In Lu H, Tang H, Wang Z, editors, Advances in Neural Networks : Proceedings 16th International Symposium on Neural Networks (ISNN 2019). Cham, Switzerland: Springer. 2019. p. 232-239. (Lecture Notes in Computer Science (LNCS)). https://doi.org/10.1007/978-3-030-22796-8_25

Author

Liu, Di ; Yu, Wenwu ; Baldi, Simone. / Broad Learning for Optimal Short-Term Traffic Flow Prediction. Advances in Neural Networks : Proceedings 16th International Symposium on Neural Networks (ISNN 2019). editor / Huchuan Lu ; Huajin Tang ; Zhanshan Wang. Cham, Switzerland : Springer, 2019. pp. 232-239 (Lecture Notes in Computer Science (LNCS)).

BibTeX

@inproceedings{0d4e968d3acf4a4a804798e03bd053a0,
title = "Broad Learning for Optimal Short-Term Traffic Flow Prediction",
abstract = "In this work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but also in terms of testing accuracy.",
keywords = "Broad Learning System, Fast least-square methods, Flat network, Traffic flow prediction",
author = "Di Liu and Wenwu Yu and Simone Baldi",
year = "2019",
doi = "10.1007/978-3-030-22796-8_25",
language = "English",
isbn = "978-3-030-22795-1",
series = "Lecture Notes in Computer Science (LNCS)",
publisher = "Springer",
pages = "232--239",
editor = "Huchuan Lu and Huajin Tang and Zhanshan Wang",
booktitle = "Advances in Neural Networks",

}

RIS

TY - GEN

T1 - Broad Learning for Optimal Short-Term Traffic Flow Prediction

AU - Liu, Di

AU - Yu, Wenwu

AU - Baldi, Simone

PY - 2019

Y1 - 2019

N2 - In this work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but also in terms of testing accuracy.

AB - In this work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but also in terms of testing accuracy.

KW - Broad Learning System

KW - Fast least-square methods

KW - Flat network

KW - Traffic flow prediction

UR - http://www.scopus.com/inward/record.url?scp=85068598247&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-22796-8_25

DO - 10.1007/978-3-030-22796-8_25

M3 - Conference contribution

SN - 978-3-030-22795-1

T3 - Lecture Notes in Computer Science (LNCS)

SP - 232

EP - 239

BT - Advances in Neural Networks

A2 - Lu, Huchuan

A2 - Tang, Huajin

A2 - Wang, Zhanshan

PB - Springer

CY - Cham, Switzerland

ER -

ID: 55272683