Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.

The network structure of Hungarian business clusters

This article investigates the attitudes to inter-firm co-operation in Hungary by analysing a special group of business networks: the business clusters. Following an overview of cluster policy, a wide range of selfproclaimed business clusters are identified. A small elite of these business networks evolves into successful, sustainable innovative business clusters. However, in the majority of cases, these consortia of interfirm co-operation are not based on a mutually satisfactory model, and as a consequence, many clusters do not survive in the longer term. The paper uses the concepts and models of social network theory in order to explain, why and under what circumstances inter-firm co-operation in clusters enhances the competitiveness of the network as a whole, or alternatively, under what circumstances the cluster remains dependent on Government subsidies. The empirical basis of the study is a thorough internet research about the Hungarian cluster movement; a questionnaire based expert survey among managers of clusters and member companies and a set of in-depth interviews among managers of self-proclaimed clusters. The last chapter analyises the applicability of social network theory in the analysis of business networks and a model involving the value chain is recommended.

Metropolitan Tourism Experience Development. Selected studies from the Tourism Network Workshop of the Regional Studies Association, held in Budapest, Hungary, 2015

On January 28-30, 2015 Corvinus University of Budapest hosted the latest workshop of the Regional Studies Association’s Tourism Research Network. The event had been held previously in Izmir, Aalborg, Warsaw, Östersund, Antalya, Leeds and Vila-seca Catalonia. The aim of the RSA research network is to examine tourism diversity from the perspective of regional development in order to identify current challenges and opportunities in a systematic manner, and hence provide the basis for a more well-informed integration of tourism in regional development strategies and move beyond political short-termism and buzzword fascination. In the frame of the network a series of workshops have been organised from various topics of destination management till rural tourism.