Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
On output independence and complementariness in rank-based multiple classifier decision systems
Download
index.pdf
Date
2001-12-01
Author
Saranlı, Afşar
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
247
views
93
downloads
Cite This
This study presents a theoretical analysis of output independence and complementariness between classifiers in a rank-based multiple classifier decision system in the context of the partitioned observation space theory. To enable such an analysis, an information theoretic interpretation of a rank-based multiple classifier system is developed and basic concepts from information theory are applied to develop measures for output independence and complementariness. It is shown that output independence of classifiers is not a requirement for achieving complementariness between these classifiers. Namely, output independence does not imply a performance improvement by combining multiple classifiers. A condition called dominance is shown to be important instead. The information theoretic measures proposed for output independence and complementariness are justified by simulated examples. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Subject Keywords
Signal Processing
,
Software
,
Artificial Intelligence
,
Computer Vision and Pattern Recognition
URI
https://hdl.handle.net/11511/38438
Journal
PATTERN RECOGNITION
DOI
https://doi.org/10.1016/s0031-3203(00)00175-8
Collections
Department of Electrical and Electronics Engineering, Article
Suggestions
OpenMETU
Core
A statistical approach to sparse multi-scale phase-based stereo
Ulusoy, İlkay (Elsevier BV, 2007-09-01)
In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic ...
Undesirable effects of output normalization in multiple classifier systems
Altincay, H; Demirekler, Mübeccel (Elsevier BV, 2003-06-01)
Incomparability of the classifier output scores is a major problem in the combination of different classification systems. In order to deal with this problem, the measurement level classifier outputs are generally normalized. However, empirical results have shown that output normalization may lead to some undesirable effects. This paper presents analyses for some most frequently used normalization methods and it is shown that the main reason for these undesirable effects of output normalization is the dimen...
Two approaches for collective learning with language games
Gülçehre, Çağlar; Bozşahin, Hüseyin Cem; Department of Cognitive Sciences (2011)
This thesis presents a defense of the view that externalism cannot be a theoretical basis of a mentalistic causal-explanatory science, even though such a theoretical basis is implicitly or explicitly adopted by many cognitive scientists. Externalism is a theory in philosophy of mind which states that mental properties are relations between the core realizers of an individual’s mental states (such as brain states) and certain things that exist outside those realizers (such as what the content of a mental sta...
Relative consistency of projective reconstructions obtained from the same image pair
Otlu, Burcak; Atalay, Mustafa Ümit; Hassanpour, Reza (World Scientific Pub Co Pte Lt, 2006-08-01)
This study obtains projective reconstructions of an object or a scene from its image pair and measures relative consistency of these projective reconstructions. 3D points are estimated from an image pair using projective and epipolar geometry. Two measures are presented for verification of projective reconstructions with each other. These measures are based on the equality of ratios between the x-, y- and z-coordinates of 3D reconstructed points which are obtained from the same corresponding points. This in...
An approach to the mean shift outlier model by Tikhonov regularization and conic programming
TAYLAN, PAKİZE; Yerlikaya-Oezkurt, Fatma; Weber, Gerhard Wilhelm (IOS Press, 2014-01-01)
In statistical research, regression models based on data play a central role; one of these models is the linear regression model. However, this model may give misleading results when data contain outliers. The outliers in linear regression can be resolved in two stages: by using the Mean Shift Outlier Model (MSOM) and by providing a new solution for this model. First, we construct a Tikhonov regularization problem for the MSOM. Then, we treat this problem using convex optimization techniques, specifically c...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. Saranlı, “On output independence and complementariness in rank-based multiple classifier decision systems,”
PATTERN RECOGNITION
, pp. 2319–2330, 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38438.