Diese Arbeit untersucht die Anwendung von Support Vektor Machines (SVMs) zur Vorhersage der Insolvenz von deutschen Unternehmen. Die Vorhersage basiert auf 24 finanziellen Kennzahlen, die in vier Kategorien unterteilt sind: Profitabilität, Fremdfinanzierung, Liquidität und Aktivität. SVMs...

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 this study, a new discriminative learning framework, called soft margin estimation (SME), is proposed for estimating the parameters of continuous density hidden Markov models (HMMs). The proposed method makes direct use of the successful ideas of margin in support vector machines to improve...

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)...

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...

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of...

The goal of this work is to introduce one of the most successful among recently developed statistical techniques - the support vector machine (SVM) - to the field of corporate bankruptcy analysis. The main emphasis is done on implementing SVMs for analysing predictors in the form of financial...

Time series analysis is an important and complex problem in machine learning and statistics. Real-world applications can consist of very large and high dimensional time series data. Support Vector Machines (SVMs) are a popular tool for the analysis of such data sets. This paper presents some SVM...

Today, most of the data in business applications is stored in relational database systems or in data warehouses built on top of relational database systems. Often, for more data is available than can be processed by standard learning algorithms in reasonable time. This paper presents an...

Today, most of the data in business applications is stored in relational databases. Relational database systems are so popular, because they offer solutions to many problems around data storage, such as efficiency, effectiveness, usability, security and multi-user support. To benefit from these...