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Explainable machine learning framework for foodborne disease outbreak prediction in Eastern Province, Saudi Arabia: case study. [PDF]

Front Public Health
Saleem Al-Ansary NF, Berekaa M, Alhotheyfa R, Almharfi A, Arakeri M, Mishra TK.   +5 more
europepmc   +1 more source

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Support vector machine classifier with huberized pinball loss

Engineering Applications of Artificial Intelligence, 2020
Abstract The original support vector machine (SVM) uses the hinge loss function, which is non-differentiable and makes the problem difficult to solve in particular for regularized SVM, such as with l 1 -regularized. On the other hand, the hinge loss is sensitive to noise. To circumvent these drawbacks, a huberized pinball loss function is
Yingyuan Xiao, Yunyan Song
exaly   +3 more sources

Support vector machines as Bayes' classifiers

Operations Research Letters, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Extreme Support Vector Machine Classifier

2008
Instead of previous SVM algorithms that utilize a kernel to evaluate the dot products of data points in a feature space, here points are explicitly mapped into a feature space by a Single hidden Layer Feedforward Network (SLFN) with its input weights randomly generated.
Qiuge Liu, Qing He 0003, Zhongzhi Shi
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Polynomial classifiers and support vector machines

1997
Polynomial support vector machines have shown a competitive performance for the problem of handwritten digit recognition. However, there is a large gap in performance vs. computing resources between the linear and the quadratic approach. By computing the complete quadratic classifier out of the quadratic support vector machine, a pivot point is found ...
Ingo Graf, Ulrich Kressel, Jürgen Franke 0003   +2 more
openaire   +1 more source