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Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 481-485
ABSTRACT
In this paper we address the problem of identifying a continuous nonlinear model from a set of discrete observations. The goal is to build a compact and accurate model of an underlying process, which is interpretable by the user, and can be also used for prediction purposes. Hinging hyper plane models are well suited to represent continuous piecewise linear models, but the hinge finding algorithm is guaranteed to converge only in local optima, and hence heavily depends on the initialization. We employ the principal Hessian direction to incorporate the geometrical information of the regression surface in the hinge finding process and can thus avoid the several random initializations proposed in the literature.
INDEX TERMS
Fasteners, Computational modeling, Regression tree analysis, Particle separators, Data models, Runtime, Buildings, prediction, hinges, principal Hessian direction, regression tree
CITATION
Anca Maria Ivanescu, Thivaharan Albin, Dirk Abel, Thomas Seidl, "Employing the Principal Hessian Direction for Building Hinging Hyperplane Models", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 481-485, doi:10.1109/ICDMW.2012.21
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