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  Effcient Approximations for Support Vector Classifiers

Kienzle, W., & Franz, M. (2004). Effcient Approximations for Support Vector Classifiers. Poster presented at 7th Tübingen Perception Conference (TWK 2004), Tübingen, Germany.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D9E7-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-65A3-1
Genre: Poster

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 Creators:
Kienzle, W1, 2, Author              
Franz, MO1, 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 face detection, support vector machines (SVM) and neural networks (NN) have been shown to outperform most other classication methods. While both approaches are learning-based, there are distinct advantages and drawbacks to each method: NNs are difcult to design and train but can lead to very small and efcient classiers. In comparison, SVM model selection and training is rather straightforward, and, more importantly, guaranteed to converge to a globally optimal (in the sense of training errors) solution. Unfortunately, SVM classiers tend to have large representations which are inappropriate for time-critical image processing applications. In this work, we examine various existing and new methods for simplifying support vector decision rules. Our goal is to obtain efcient classiers (as with NNs) while keeping the numerical and statistical advantages of SVMs. For a given SVM solution, we compute a cascade of approximations with increasing complexities. Each classier is tuned so that the detection rate is near 100. At run-time, the rst (simplest) detector is evaluated on the whole image. Then, any subsequent classier is applied only to those positions that have been classied as positive throughout all previous stages. The false positive rate at the end equals that of the last (i.e. most complex) detector. In contrast, since many image positions are discarded by lower-complexity classiers, the average computation time per patch decreases signicantly compared to the time needed for evaluating the highest-complexity classier alone.

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 Dates: 2004-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: KienzleF2004
 Degree: -

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Title: 7th Tübingen Perception Conference (TWK 2004)
Place of Event: Tübingen, Germany
Start-/End Date: 2004-01-30 - 2004-02-01

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Title: 7th Tübingen Perception Conference: TWK 2004
Source Genre: Proceedings
 Creator(s):
Bülthoff, HH1, Editor            
Mallot, HA, Editor            
Ulrich, RD, Editor
Wichmann, FA1, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Kirchentellinsfurt, Germany : Knirsch
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 68 Identifier: ISBN: 3-927091-68-5