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/works › Article in conference proceedings › Research › peer-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
UR - http://www.scopus.com/inward/record.url?scp=85061112398&partnerID=8YFLogxK
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 -
