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  Maximum-Margin Feature Combination for Detection and Categorization

Bakır, G., Wu, M., & Eichhorn, J.(2005). Maximum-Margin Feature Combination for Detection and Categorization. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D707-B Version Permalink: http://hdl.handle.net/21.11116/0000-0006-C581-9
Genre: Report

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 Creators:
Bakır, GH1, 2, Author              
Wu, M1, 2, Author              
Eichhorn, J1, 2, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: In this paper we are concerned with the optimal combination of features of possibly different types for detection and estimation tasks in machine vision. We propose to combine features such that the resulting classifier maximizes the margin between classes. In contrast to existing approaches which are non-convex and/or generative we propose to use a discriminative model leading to convex problem formulation and complexity control. Furthermore we assert that decision functions should not compare apples and oranges by comparing features of different types directly. Instead we propose to combine different similarity measures for each different feature type. Furthermore we argue that the question: ”Which feature type is more discriminative for task X?” is ill-posed and show empirically that the answer to this question might depend on the complexity of the decision function.

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 Dates: 2005
 Publication Status: Published in print
 Pages: 8
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Identifiers: BibTex Citekey: 3471
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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