Dissimilarity-based multiple instance learning

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.
TitelStructural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings
RedaktørerEdwin R. Hancock, Richard C. Wilson, Terry Windeatt, Ilkay Ulusoy, Francisco Escolano
Antal sider10
ISBN (Trykt)978-3-642-14979-5
ISBN (Elektronisk)978-3-642-14980-1
StatusUdgivet - 2010
BegivenhedJoint IAPR International Workshop on Structural, Sytactic and Statistical Pattern Recognition - Cesme, Tyrkiet
Varighed: 18 aug. 201020 aug. 2010


KonferenceJoint IAPR International Workshop on Structural, Sytactic and Statistical Pattern Recognition
NavnLecture notes in computer science

ID: 19257459