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Linear Separability Analysis for Stacked Generalization Architecture
Date
2009-04-11
Author
Ozay, Mete
Vural, Fatos T. Yarman
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Stacked Generalization algorithm aims to increase the individual classification performances of the classifiers by combining the information obtained from various classifiers in a multilayer architecture by either linear or nonlinear techniques. Performance of the algorithm varies depending on the application domains and the space analyses that affect the classification performances could riot be applied successfully.
Subject Keywords
Testing
,
Nonhomogeneous media
,
Performance analysis
,
Algorithm design and analysis
URI
https://hdl.handle.net/11511/64453
Collections
Department of Computer Engineering, Conference / Seminar
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M. Ozay and F. T. Y. Vural, “Linear Separability Analysis for Stacked Generalization Architecture,” 2009, p. 786, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64453.