Frame-based Optimal Design

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

Frame-based Optimal Design. / Mair, Sebastian; Rudolph, Yannick; Closius, Vanessa et al.
Machine learning and knowledge discovery in databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings. ed. / Michele Berlingerio; Francesco Bonchi; Thomas Gärtner; Neil Hurley; Georgiana Ifrim. Vol. 2 Cham: Springer Nature AG, 2019. p. 447-463 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11052 LNAI).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Mair, S, Rudolph, Y, Closius, V & Brefeld, U 2019, Frame-based Optimal Design. in M Berlingerio, F Bonchi, T Gärtner, N Hurley & G Ifrim (eds), Machine learning and knowledge discovery in databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings. vol. 2, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11052 LNAI, Springer Nature AG, Cham, pp. 447-463, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - 2018, Dublin, Ireland, 10.09.18. https://doi.org/10.1007/978-3-030-10928-8_27

APA

Mair, S., Rudolph, Y., Closius, V., & Brefeld, U. (2019). Frame-based Optimal Design. In M. Berlingerio, F. Bonchi, T. Gärtner, N. Hurley, & G. Ifrim (Eds.), Machine learning and knowledge discovery in databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings (Vol. 2, pp. 447-463). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11052 LNAI). Springer Nature AG. https://doi.org/10.1007/978-3-030-10928-8_27

Vancouver

Mair S, Rudolph Y, Closius V, Brefeld U. Frame-based Optimal Design. In Berlingerio M, Bonchi F, Gärtner T, Hurley N, Ifrim G, editors, Machine learning and knowledge discovery in databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings. Vol. 2. Cham: Springer Nature AG. 2019. p. 447-463. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-10928-8_27

Bibtex

@inbook{56a3bd71d1c94f8dad5722c23ef47d38,
title = "Frame-based Optimal Design",
abstract = "Optimal experimental design (OED) addresses the problem of selecting an optimal subset of the training data for learning tasks. In this paper, we propose to efficiently compute OED by leveraging the geometry of data: We restrict computations to the set of instances lying on the border of the convex hull of all data points. This set is called the frame. We (i) provide the theoretical basis for our approach and (ii) show how to compute the frame in kernel-induced feature spaces. The latter allows us to sample optimal designs for non-linear hypothesis functions without knowing the explicit feature mapping. We present empirical results showing that the performance of frame-based OED is often on par or better than traditional OED approaches, but its solution can be computed up to twenty times faster.",
keywords = "Business informatics, Active learning, Fast approximation, Frame, Optimal experimental design, Regression",
author = "Sebastian Mair and Yannick Rudolph and Vanessa Closius and Ulf Brefeld",
year = "2019",
month = jan,
day = "23",
doi = "10.1007/978-3-030-10928-8_27",
language = "English",
isbn = "978-3-030-10927-1",
volume = "2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature AG",
pages = "447--463",
editor = "Michele Berlingerio and { Bonchi}, Francesco and Thomas G{\"a}rtner and { Hurley}, Neil and Georgiana Ifrim",
booktitle = "Machine learning and knowledge discovery in databases",
address = "Germany",
note = "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - 2018 ; Conference date: 10-09-2018 Through 14-09-2018",

}

RIS

TY - CHAP

T1 - Frame-based Optimal Design

AU - Mair, Sebastian

AU - Rudolph, Yannick

AU - Closius, Vanessa

AU - Brefeld, Ulf

PY - 2019/1/23

Y1 - 2019/1/23

N2 - Optimal experimental design (OED) addresses the problem of selecting an optimal subset of the training data for learning tasks. In this paper, we propose to efficiently compute OED by leveraging the geometry of data: We restrict computations to the set of instances lying on the border of the convex hull of all data points. This set is called the frame. We (i) provide the theoretical basis for our approach and (ii) show how to compute the frame in kernel-induced feature spaces. The latter allows us to sample optimal designs for non-linear hypothesis functions without knowing the explicit feature mapping. We present empirical results showing that the performance of frame-based OED is often on par or better than traditional OED approaches, but its solution can be computed up to twenty times faster.

AB - Optimal experimental design (OED) addresses the problem of selecting an optimal subset of the training data for learning tasks. In this paper, we propose to efficiently compute OED by leveraging the geometry of data: We restrict computations to the set of instances lying on the border of the convex hull of all data points. This set is called the frame. We (i) provide the theoretical basis for our approach and (ii) show how to compute the frame in kernel-induced feature spaces. The latter allows us to sample optimal designs for non-linear hypothesis functions without knowing the explicit feature mapping. We present empirical results showing that the performance of frame-based OED is often on par or better than traditional OED approaches, but its solution can be computed up to twenty times faster.

KW - Business informatics

KW - Active learning

KW - Fast approximation

KW - Frame

KW - Optimal experimental design

KW - Regression

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U2 - 10.1007/978-3-030-10928-8_27

DO - 10.1007/978-3-030-10928-8_27

M3 - Article in conference proceedings

AN - SCOPUS:85061112398

SN - 978-3-030-10927-1

VL - 2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 447

EP - 463

BT - Machine learning and knowledge discovery in databases

A2 - Berlingerio, Michele

A2 - Bonchi, Francesco

A2 - Gärtner, Thomas

A2 - Hurley, Neil

A2 - Ifrim, Georgiana

PB - Springer Nature AG

CY - Cham

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - 2018

Y2 - 10 September 2018 through 14 September 2018

ER -