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Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise)...
We propose novel algorithms for solving the so-called Support Vector Multiple Kernel Learning problem and show how they can be used to understand the resulting support vector decision function. While classical kernel-based algorithms (such as SVMs) are based on a single kernel, in Multiple Kernel Learning a quadratically-constraint quadratic program is solved in order to find a sparse convex combination...
HintergrundMit Prilocain steht in der Tageschirurgie ein kurz wirksames Lokalanästhetikum für Spinalanästhesien ohne erhöhtes Risiko für transiente neurologische Störungen (TNS) zur Verfügung.Patienten und MethodenIn randomisierter Zuordnung erhielten 88 Patienten mit Eingriffen an der unteren Extremität in Spinalanästhesie 15 mg 0,5%iges hyperbares Bupivacain oder 60 mg 2%iges hyperbares Prilocain...
Recently ensemble methods like ADABOOST have been applied successfully in many problems, while seemingly defying the problems of overfitting. ADABOOST rarely overfits in the low noise regime, however, we show that it clearly does so for higher noise levels. Central to the understanding of this fact is the margin distribution. ADABOOST can be viewed as a constraint gradient descent in an error function...
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