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  Deterministic Annealing for Multiple-Instance Learning

Gehler, P., & Chapelle, O. (2007). Deterministic Annealing for Multiple-Instance Learning. Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007), 123-130.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CE79-3 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CE7A-1
Genre: Conference Paper

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 Creators:
Gehler, PV1, Author           
Chapelle, O1, Author           
Meila X. Shen, M., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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
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 Identifiers: URI: http://jmlr.csail.mit.edu/proceedings/papers/v2/gehler07a.html
BibTex Citekey: 4270
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Title: 11th International Conference on Artificial Intelligence and Statistics
Place of Event: San Juan, Puerto Rico
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Title: Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Source Genre: Journal
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 123 - 130 Identifier: -