Abstract This paper proposes a rating methodology that is based on a non-linear classification method, a support vector machine, and a non-parametric isotonic regression for mapping rating scores into probabilities of default. We also propose a four data set model validation and training...

This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive SVM. In practice, however, because these criteria require especially...

In this paper, we present an analysis of the results of a study into wholesale (spot) electricity price forecasting utilising Neural Networks (NNs) and Support Vector Machines (SVM). Frequent regulatory changes in electricity markets and the quickly evolving market participant pricing (bidding)...

This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and...

Graphical data representation is an important tool for model selection in bankruptcy analysis since the problem is highly non-linear and its numerical representation is much less transparent. In classical rating models a convenient representation of ratings in a closed form is possible reducing...

This thesis presents and compares the performance of two recently developed classification methods namely the Spatial Stagewise Aggregation procedure and Support Vector Machines. Both techniques are convenient for the application to corporate bankruptcy analysis, in terms of calculation of...

In regression, the desired estimate of y|x is not always given by a conditional mean, althoughthis is most common. Sometimes one wants to obtain a good estimate that satisfies the propertythat a proportion, t, of y|x, will be below the estimate. For t = 0.5 this is an estimate of themedian. What...

We present a novel approach for measuring democracy based on Support Vector Machines, a mathematical algorithm for pattern recognition. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-interval and enables a very detailed measurement of democracy for 188 countries...

Institutional settings play a key role in shaping land cover and land use. Our goal was to understand the effects of institutional changes on agricultural land abandonment in different countries of Eastern Europe and the former Soviet Union after the collapse of socialism. We studied ∼273 800...

We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data are mapped to quantum...