Search Results - subject:"vector quantization"

Rudd, Ralph; McWalter, Thomas A.; Kienitz, Jörg; … - In: The journal of computational finance 25 (2022) 4, pp. 55-78

Persistent link: https://www.econbiz.de/10014546287

Optimal multi-product supplier selection under stochastic demand with service level and budget constraints using learning vector quantization neural network

HormozzadehGhalati, Hajar; Abbasi, Alireza; … - In: RAIRO / Operations research 53 (2019) 5, pp. 1709-1720

Persistent link: https://www.econbiz.de/10012146051

Recursive marginal quantization of higher-order schemes

McWalter, Thomas A.; Rudd, Ralph; Kienitz, Jörg; … - In: Quantitative finance 18 (2018) 4, pp. 693-706

Persistent link: https://www.econbiz.de/10011906463

Comparative Performance Analysis of Optimization Techniques on Vector Quantization for Image Compression

Chiranjeevi, Karri; Jena, Umaranjan; Dash, Sonali - In: International Journal of Computer Vision and Image … 7 (2017) 1, pp. 19-43

Linde-Buzo-Gray (LBG) Vector Quantization (VQ), technically generates local codebook after many runs on different sets …

Persistent link: https://www.econbiz.de/10012043822

Does Non-linearity Matter in Retail Credit Risk Modeling?

Jagric, Timotej; Jagric, Vita; Kracun, Davorin - In: Czech Journal of Economics and Finance (Finance a uver) 61 (2011) 4, pp. 384-402

-linear relationships between credit risk and explanatory variables, we use a learning vector quantization (LVQ) neural network. The model …

Persistent link: https://www.econbiz.de/10009251306

Multifractional stochastic volatility models

Corlay, Sylvain; Lebovits, Joachim; Lévy Véhel, Jacques - In: Mathematical finance : an international journal of … 24 (2014) 2, pp. 364-402

Persistent link: https://www.econbiz.de/10010357370

An Application of Clustering Analysis to International Private Indebtedness

Andre, Monteiro; Dionisio, Carneiro; Carlos, Pedreira - EconWPA - 2005

This paper presents a procedure for clustering analysis that combines Kohone’s Self organizing Feature Map (SOFM) and statistical schemes. The idea is to cluster the data in two stages: run SOFM and then minimize the segmentation dispersion. The advantages of proposed procedure will be...

Persistent link: https://www.econbiz.de/10005412997