Response Surface Methodology for Optimizing Hyper Parameters - Claus Weihs; Karsten Luebke; Irina Czogiel (Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, 2006)
The performance of an algorithm often largely depends on some hyper parameter which should be optimized before its usage. Since most conventional optimization methods suffer from some drawbacks, we developed an alternative way to find the best hyper parameter values. Contrary to the well known procedures, the new optimization algorithm is based on statistical methods since it uses a combination of Linear Mixed Effect Models and Response Surface Methodology techniques. In particular, the Method of Steepest Ascent which is well known for the case of an Ordinary Least Squares setting and a linear response surface has been generalized to be applicable for repeated measurements situations and for response surfaces of order o ?Ü 2.
Keywords: 519; repeated measurements; Random Intercepts Model; deterministic error terms; Method of Steepest Ascent; Support Vector Machine