Standard
Resilient approximation of kernel classifiers. / Suttorp, Thorsten; Igel, Christian.
Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I. ed. / Joaquim Marques de Sá; Lius A. Alexandre; Włodzisław Duch; Danilo Mandic. Vol. Part I Springer, 2007. p. 139-148 (Lecture notes in computer science, Vol. 4668).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Suttorp, T
& Igel, C 2007,
Resilient approximation of kernel classifiers. in JM de Sá, LA Alexandre, W Duch & D Mandic (eds),
Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I. vol. Part I, Springer, Lecture notes in computer science, vol. 4668, pp. 139-148, 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, Portugal,
09/09/2007.
https://doi.org/10.1007/978-3-540-74690-4_15APA
Suttorp, T.
, & Igel, C. (2007).
Resilient approximation of kernel classifiers. In J. M. de Sá, L. A. Alexandre, W. Duch, & D. Mandic (Eds.),
Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I (Vol. Part I, pp. 139-148). Springer. Lecture notes in computer science Vol. 4668
https://doi.org/10.1007/978-3-540-74690-4_15Vancouver
Suttorp T
, Igel C.
Resilient approximation of kernel classifiers. In de Sá JM, Alexandre LA, Duch W, Mandic D, editors, Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I. Vol. Part I. Springer. 2007. p. 139-148. (Lecture notes in computer science, Vol. 4668).
https://doi.org/10.1007/978-3-540-74690-4_15Author
Suttorp, Thorsten ; Igel, Christian. / Resilient approximation of kernel classifiers. Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I. editor / Joaquim Marques de Sá ; Lius A. Alexandre ; Włodzisław Duch ; Danilo Mandic. Vol. Part I Springer, 2007. pp. 139-148 (Lecture notes in computer science, Vol. 4668).
Bibtex
@inproceedings{b01ca9e054f94f7bbda9ebccac4aafd6,
title = "Resilient approximation of kernel classifiers",
abstract = "Trained support vector machines (SVMs) have a slow run-time classification speed if the classification problem is noisy and the sample data set is large. Approximating the SVM by a more sparse function has been proposed to solve to this problem. In this study, different variants of approximation algorithms are empirically compared. It is shown that gradient descent using the improved Rprop algorithm increases the robustness of the method compared to fixed-point iteration. Three different heuristics for selecting the support vectors to be used in the construction of the sparse approximation are proposed. It turns out that none is superior to random selection. The effect of a finishing gradient descent on all parameters of the sparse approximation is studied.",
author = "Thorsten Suttorp and Christian Igel",
year = "2007",
doi = "10.1007/978-3-540-74690-4_15",
language = "English",
isbn = "978-3-540-74689-8",
volume = "Part I",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "139--148",
editor = "{de S{\'a}}, {Joaquim Marques} and Alexandre, {Lius A.} and W{\l}odzis{\l}aw Duch and Danilo Mandic",
booktitle = "Artificial Neural Networks – ICANN 2007",
address = "Switzerland",
note = "17th International Conference on Artificial Neural Networks, ICANN 2007 ; Conference date: 09-09-2007 Through 13-09-2007",
}
RIS
TY - GEN
T1 - Resilient approximation of kernel classifiers
AU - Suttorp, Thorsten
AU - Igel, Christian
PY - 2007
Y1 - 2007
N2 - Trained support vector machines (SVMs) have a slow run-time classification speed if the classification problem is noisy and the sample data set is large. Approximating the SVM by a more sparse function has been proposed to solve to this problem. In this study, different variants of approximation algorithms are empirically compared. It is shown that gradient descent using the improved Rprop algorithm increases the robustness of the method compared to fixed-point iteration. Three different heuristics for selecting the support vectors to be used in the construction of the sparse approximation are proposed. It turns out that none is superior to random selection. The effect of a finishing gradient descent on all parameters of the sparse approximation is studied.
AB - Trained support vector machines (SVMs) have a slow run-time classification speed if the classification problem is noisy and the sample data set is large. Approximating the SVM by a more sparse function has been proposed to solve to this problem. In this study, different variants of approximation algorithms are empirically compared. It is shown that gradient descent using the improved Rprop algorithm increases the robustness of the method compared to fixed-point iteration. Three different heuristics for selecting the support vectors to be used in the construction of the sparse approximation are proposed. It turns out that none is superior to random selection. The effect of a finishing gradient descent on all parameters of the sparse approximation is studied.
U2 - 10.1007/978-3-540-74690-4_15
DO - 10.1007/978-3-540-74690-4_15
M3 - Article in proceedings
AN - SCOPUS:38149089662
SN - 978-3-540-74689-8
VL - Part I
T3 - Lecture notes in computer science
SP - 139
EP - 148
BT - Artificial Neural Networks – ICANN 2007
A2 - de Sá, Joaquim Marques
A2 - Alexandre, Lius A.
A2 - Duch, Włodzisław
A2 - Mandic, Danilo
PB - Springer
T2 - 17th International Conference on Artificial Neural Networks, ICANN 2007
Y2 - 9 September 2007 through 13 September 2007
ER -
