Bioinformatics is the science of managing, mining and interpreting information from biological sequences and structures. In this paper, we discuss two data-mining techniques that can be applied in bioinformatics: Neural Networks (NN) and Support Vector Machines (SVMs), and their application in...

Data description and classification are important tasks in supervised learning. In this study, three supervised learning methods such as k-nearest neighbour (k-NN), support vector data description (SVDD) and support vector machine (SVM) are considered because they do not suffer from the problem...

We propose a support vector machine (SVM)-based structural model to forecast the collapse of banking institutions in the USA using publicly disclosed information from their financial statements on a four-year rolling window. In our approach, the optimum input variable set is defined from a large...

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...

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...