@article{article_645223, title={COMPARISON OF MACHINE LEARNING TECHNIQUES FOR ANALYZING BANKS’ FINANCIAL DISTRESS}, journal={Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi}, volume={19}, pages={291–304}, year={2016}, DOI={10.31795/baunsobed.645223}, author={Altınırmak, Serpil and Karamaşa, Çağlar}, keywords={Finansal Başarısızlık,Veri Madenciliği,Destek Vektör Makineleri}, abstract={Analyzing banks’ financial distress has gained great importance due to their importance in national economy and caused sociological and economic results. Support Vector Machines SVM and Neural Networks NN , known as machine learning methods, are applied for classifying banks as an early warning of financial distress. A case study which is taking thirty private equity commercial banks’ five year data and financial ratios, is carried out. As a result SVM obtains better classification ratio than NNs}, number={36}, publisher={Balıkesir Üniversitesi}