Publications
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Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation Ghent University
Statistical discrimination of steroid profiles in doping control with support vector machines Ghent University
Statistical discrimination of steroid profiles with support vector machines in doping control Ghent University
Enhanced pedotransfer functions with support vector machines to predict water retention of calcareous soil Ghent University
Support vector machines Hasselt University
A support vector machine (SVM) is a supervised machine learning (ML) method capable of learning from data and making decisions. The fundamental principles of the SVM were already introduced in the 1960s by Vapnik and Chervonenkis 1 in a theory that was further developed throughout the next decennia. However, it was only in the 1990s that SVMs attracted greater attention from the scientific community , and this was attributed to 2 significant ...
Frequency-domain least-squares support vector machines to deal with correlated errors when identifying linear time-varying systems Vrije Universiteit Brussel
A Least-Squares Support Vector Machine (LS-SVM) estimator, formulated in the frequency domain is proposed to identify linear time-varying dynamic systems. The LS-SVM aims at learning the structure of the time variation in a data driven way. The frequency domain is chosen for its superior robustness w.r.t. correlated errors for the calibration of the hyper parameters of the model. The time-domain and the frequency-domain implementations are ...
On consistency and robustness properties of Support Vector Machines for heavy-tailed distributions Vrije Universiteit Brussel
Support Vector Machines (SVMs) are known to be consistent and robust for classification and regression if they are based on a Lipschitz continuous loss function and on a bounded kernel with a dense and separable reproducing kernel Hilbert space. These facts are even true in the regression context for unbounded output spaces, if the target function f is integrable with respect to the marginal distribution of
the input variable X and if the ...
the input variable X and if the ...