The nature of statistical learning theory
The nature of statistical learning theory
A tutorial on support vector regression
Statistics and Computing
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Identification of a periodic time series from an environmental proxy record
Computers & Geosciences
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
Introduction: Modelling and control in biomedical systems
Computer Methods and Programs in Biomedicine
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To improve our understanding of the climate process and to assess the human impact on current global warming, past climate reconstruction is essential. The chemical composition of a bivalve shell is strongly coupled to environmental variations and therefore ancient shells are potential climate archives. The nonlinear nature of the relation between environmental condition (e.g. the seawater temperature) and proxy composition makes it hard to predict the former from the latter, however. In this paper we compare the ability of three nonlinear system identification methods to reconstruct the ambient temperature from the chemical composition of a shell. The comparison shows that nonlinear multi-proxy approaches are potentially useful tools for climate reconstructions and that manifold based methods result in smoother and more precise temperature reconstruction.