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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 352355.0, MPI für biologische Kybernetik / Biologische Kybernetik
Deterministic Annealing for
Multiple-Instance Learning
Authors:Gehler, P.V.; Chapelle, O.
Date of Publication (YYYY-MM-DD):2007-03
Title of Proceedings:Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Start Page:123
End Page:130
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:In this paper we demonstrate how deterministic annealing can be
applied to different SVM formulations of the multiple-instance
learning (MIL) problem. Our results show that we find better local
minima compared to the heuristic methods those problems are usually
solved with. However this does not always translate into a better test
error suggesting an inadequacy of the objective function. Based on
this finding we propose a new objective function which together with
the deterministic annealing algorithm finds better local minima and
achieves better performance on a set of benchmark
datasets. Furthermore the results also show how the structure of MIL
datasets influence the performance of MIL algorithms and we discuss
how future benchmark datasets for the MIL problem should be designed.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:4270
URL:http://jmlr.csail.mit.edu/proceedings/papers/v2/
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