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  Deterministic Annealing for Multiple-Instance Learning

Gehler, P., & Chapelle, O. (2007). Deterministic Annealing for Multiple-Instance Learning. In M. Meila, & X. Shen (Eds.), Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico (pp. 123-130). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CE79-3 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-E8BC-4
Genre: Conference Paper

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http://proceedings.mlr.press/v2/gehler07a/gehler07a.pdf (Publisher version)
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 Creators:
Gehler, PV1, 2, Author           
Chapelle, O1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2007-03
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 4270
 Degree: -

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Title: 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Place of Event: San Juan, Puerto Rico
Start-/End Date: 2007-03-21 - 2007-03-24

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Source 1

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Title: Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico
Source Genre: Proceedings
 Creator(s):
Meila, M, Editor
Shen, X, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 123 - 130 Identifier: -

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
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Pages: - Volume / Issue: 2 Sequence Number: - Start / End Page: - Identifier: -