Standard
Dissimilarity-based multiple instance learning. / Sørensen, Lauge; Loog, Marco; Tax, David M. J.; Lee, Wan-Jui; de Bruijne, Marleen; Duin, Robert P. W.
Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings. red. / Edwin R. Hancock; Richard C. Wilson; Terry Windeatt; Ilkay Ulusoy; Francisco Escolano. Springer, 2010. s. 129-138 (Lecture notes in computer science; Nr. 6218).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Sørensen, L, Loog, M, Tax, DMJ, Lee, W-J
, de Bruijne, M & Duin, RPW 2010,
Dissimilarity-based multiple instance learning. i ER Hancock, RC Wilson, T Windeatt, I Ulusoy & F Escolano (red),
Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings. Springer, Lecture notes in computer science, nr. 6218, s. 129-138, Joint IAPR International Workshop on Structural, Sytactic and Statistical Pattern Recognition, Cesme, Tyrkiet,
18/08/2010.
https://doi.org/10.1007/978-3-642-14980-1_12APA
Sørensen, L., Loog, M., Tax, D. M. J., Lee, W-J.
, de Bruijne, M., & Duin, R. P. W. (2010).
Dissimilarity-based multiple instance learning. I E. R. Hancock, R. C. Wilson, T. Windeatt, I. Ulusoy, & F. Escolano (red.),
Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings (s. 129-138). Springer. Lecture notes in computer science Nr. 6218
https://doi.org/10.1007/978-3-642-14980-1_12Vancouver
Sørensen L, Loog M, Tax DMJ, Lee W-J
, de Bruijne M, Duin RPW.
Dissimilarity-based multiple instance learning. I Hancock ER, Wilson RC, Windeatt T, Ulusoy I, Escolano F, red., Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings. Springer. 2010. s. 129-138. (Lecture notes in computer science; Nr. 6218).
https://doi.org/10.1007/978-3-642-14980-1_12Author
Sørensen, Lauge ; Loog, Marco ; Tax, David M. J. ; Lee, Wan-Jui ; de Bruijne, Marleen ; Duin, Robert P. W. / Dissimilarity-based multiple instance learning. Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings. red. / Edwin R. Hancock ; Richard C. Wilson ; Terry Windeatt ; Ilkay Ulusoy ; Francisco Escolano. Springer, 2010. s. 129-138 (Lecture notes in computer science; Nr. 6218).
Bibtex
@inproceedings{2232fd00489811df928f000ea68e967b,
title = "Dissimilarity-based multiple instance learning",
abstract = "In this paper, we propose to solve multiple instance learning problems using a dissimilarity representation of the objects. Once the dissimilarity space has been constructed, the problem is turned into a standard supervised learning problem that can be solved with a general purpose supervised classifier. This approach is less restrictive than kernel-based approaches and therefore allows for the usage of a wider range of proximity measures. Two conceptually different types of dissimilarity measures are considered: one based on point set distance measures and one based on the earth movers distance between distributions of within- and between set point distances, thereby taking relations within and between sets into account. Experiments on five publicly available data sets show competitive performance in terms of classification accuracy compared to previously published results.",
author = "Lauge S{\o}rensen and Marco Loog and Tax, {David M. J.} and Wan-Jui Lee and {de Bruijne}, Marleen and Duin, {Robert P. W.}",
year = "2010",
doi = "10.1007/978-3-642-14980-1_12",
language = "English",
isbn = "978-3-642-14979-5",
series = "Lecture notes in computer science",
publisher = "Springer",
number = "6218",
pages = "129--138",
editor = "Hancock, {Edwin R.} and Wilson, {Richard C.} and Terry Windeatt and Ilkay Ulusoy and Francisco Escolano",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition",
address = "Switzerland",
note = "Joint IAPR International Workshop on Structural, Sytactic and Statistical Pattern Recognition, SSPR SPR 2010 ; Conference date: 18-08-2010 Through 20-08-2010",
}
RIS
TY - GEN
T1 - Dissimilarity-based multiple instance learning
AU - Sørensen, Lauge
AU - Loog, Marco
AU - Tax, David M. J.
AU - Lee, Wan-Jui
AU - de Bruijne, Marleen
AU - Duin, Robert P. W.
PY - 2010
Y1 - 2010
N2 - In this paper, we propose to solve multiple instance learning problems using a dissimilarity representation of the objects. Once the dissimilarity space has been constructed, the problem is turned into a standard supervised learning problem that can be solved with a general purpose supervised classifier. This approach is less restrictive than kernel-based approaches and therefore allows for the usage of a wider range of proximity measures. Two conceptually different types of dissimilarity measures are considered: one based on point set distance measures and one based on the earth movers distance between distributions of within- and between set point distances, thereby taking relations within and between sets into account. Experiments on five publicly available data sets show competitive performance in terms of classification accuracy compared to previously published results.
AB - In this paper, we propose to solve multiple instance learning problems using a dissimilarity representation of the objects. Once the dissimilarity space has been constructed, the problem is turned into a standard supervised learning problem that can be solved with a general purpose supervised classifier. This approach is less restrictive than kernel-based approaches and therefore allows for the usage of a wider range of proximity measures. Two conceptually different types of dissimilarity measures are considered: one based on point set distance measures and one based on the earth movers distance between distributions of within- and between set point distances, thereby taking relations within and between sets into account. Experiments on five publicly available data sets show competitive performance in terms of classification accuracy compared to previously published results.
U2 - 10.1007/978-3-642-14980-1_12
DO - 10.1007/978-3-642-14980-1_12
M3 - Article in proceedings
SN - 978-3-642-14979-5
T3 - Lecture notes in computer science
SP - 129
EP - 138
BT - Structural, Syntactic, and Statistical Pattern Recognition
A2 - Hancock, Edwin R.
A2 - Wilson, Richard C.
A2 - Windeatt, Terry
A2 - Ulusoy, Ilkay
A2 - Escolano, Francisco
PB - Springer
T2 - Joint IAPR International Workshop on Structural, Sytactic and Statistical Pattern Recognition
Y2 - 18 August 2010 through 20 August 2010
ER -
