Linear Separability Analysis for Stacked Generalization Architecture

Date

2009-04-11

Author

Ozay, Mete
Vural, Fatos T. Yarman

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This work is licensed under a 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

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

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.