Publications
Chosen filters:
Chosen filters:
Flood resilience from an evolutionary perspective: how to deal with flexibility vs. robustness in spatial planning Ghent University
A systematic assessment of the variability of matrix effects in LC-MS/MS analysis of ergot alkaloids in cereals and evaluation of method robustness Ghent University
On algorithm and robustness in a non-standard sense Ghent University
An activity-based CA model for Flanders, Belgium. Robustness analysis and improved distance computation in a variable grid representation Vrije Universiteit Brussel KU Leuven
Cellular automata (CA) models are increasingly applied for simulating land-use change in urban areas. However, in areas with strongly mixed land uses, like Flanders, Belgium, different types and intensities of human activity occur within a single dominant land use. This is in conflict with the discrete and dominant land-use states applied in CA. The direct modelling of the intensity of activities (population density and employment in different ...
Robustness tests of a model based predictive control strategy for depth of anesthesia regulation in a propofol to bispectral index framework Ghent University
This paper verifies the robustness of a model based predictive control scheme for depth of anesthesia (DOA) regulation. The manipulated variable is Propofol, which is used in a Model based Predictive Control (MPC) algorithm for automatic induction and control of DOA. In turn, DOA is evaluated by means of the Bispectral index (BIS). The simulation tests are performed on a set of 17 virtually generated realistic patients with significantly varying ...
A Review on Consistency and Robustness Properties of Support Vector Machines for Heavy-Tailed Distributions Vrije Universiteit Brussel
Support vector machines (SVMs) belong to the class of modern statistical machine learning techniques and can be described as M-estimatorswith aHilbert norm regularization term for functions. SVMs are consistent and robust for classification and regression purposes if based on a Lipschitz continuous loss and a bounded continuous kernel with a dense reproducing kernel Hilbert space. For regression, one of the conditions used is that the output ...
evolution-inspired approaches for engineering emergent robustness in an uncertain dynamic world Vrije Universiteit Brussel
nvt
A review on consistency and robustness properties of support vector machines for heavy-tailed distributions Ghent University
Support vector machines (SVMs) belong to the class of modern statistical machine learning techniques and can be described as M-estimators with a Hilbert norm regularization term for functions. SVMs are consistent and robust for classification and regression purposes if based on a Lipschitz continuous loss and a bounded continuous kernel with a dense reproducing kernel Hilbert space. For regression, one of the conditions used is that the output ...
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 ...