Atıf İçin Kopyala
Kocaoğlu A.
APPLIED SCIENCES, cilt.14, sa.9, ss.1-21, 2024 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
14
Sayı:
9
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Basım Tarihi:
2024
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Doi Numarası:
10.3390/app14093641
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Dergi Adı:
APPLIED SCIENCES
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
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Sayfa Sayıları:
ss.1-21
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Dokuz Eylül Üniversitesi Adresli:
Evet
Özet
While traditional support vector regression (SVR) models rely on loss functions tailored to specific noise distributions, this research explores an alternative approach: ε" role="presentation" >𝜀𝜀
-ln SVR, which uses a loss function based on the natural logarithm of the hyperbolic cosine function (lncosh). This function exhibits optimality for a broader family of noise distributions known as power-raised hyperbolic secants (PHSs). We derive the dual formulation of the ε" role="presentation" >𝜀𝜀
-ln SVR model, which reveals a nonsmooth, nonlinear convex optimization problem. To efficiently overcome these complexities, we propose a novel sequential minimal optimization (SMO)-like algorithm with an innovative working set selection (WSS) procedure. This procedure exploits second-order (SO)-like information by minimizing an upper bound on the second-order Taylor polynomial approximation of consecutive loss function values. Experimental results on benchmark datasets demonstrate the effectiveness of both the ε" role="presentation" >𝜀𝜀
-ln SVR model with its lncosh loss and the proposed SMO-like algorithm with its computationally efficient WSS procedure. This study provides a promising tool for scenarios with different noise distributions, extending beyond the commonly assumed Gaussian to the broader PHS family.