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2 December 2005 Regularization regression based on real coded genetic algorithms
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Proceedings Volume 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications; 60450Z (2005) https://doi.org/10.1117/12.650685
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Abstract
Real coded Genetic Algorithms (RGAs) have received a great deal of attention regarding their potential as optimization techniques for complex functions. Regularization was applied to solve ill-posed problems with an additional information about the solutions. In this paper, we introduce a new method named Regularization Regression based on RGAs to rebuild traditional regression methods, in which different regularization terms, regularization parameters and proper loss functions are designed flexibly according to prior knowledge of different problems.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yugang Tian, Peijun Shi, and Wendong Nie "Regularization regression based on real coded genetic algorithms", Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 60450Z (2 December 2005); https://doi.org/10.1117/12.650685
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