Objective: Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction M...

In the paper, orthogonal transforms based on proposed symmetric, matrices are created. These can be considered as generalized Walsh–Hadamard Transforms. The simplicity of calculating forward and inverse is one important features presented approach. conditions for creating defined. It shown that selection elements an matrix meets given conditions, it necessary to select only a limited number ele...

We study the performance in cooperative diversity protocols employing relay selection, utilizing the diversity-multiplexing tradeoff. We note that for the class of orthogonal cooperation protocols studied by Laneman and Wornell, there is no loss in performance if only one (suitably chosen) relay participates in cooperation. This observation also holds in systems which incorporate decision feedb...

An orthogonal forward selection (OFS) algorithm based on the leaveone-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to sp...

International Journal of Systems Science Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713697751 A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate X. Hong a; S. Chen b; C. J. Harris b a School of Systems Engin...

This paper introduces an orthogonal forward regression (OFR) model structure selection algorithm based on the Mestimators. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified GramSchmidt procedure. In this manner the OFR algorithm is extended to bad data conditions with improved performance due to M-estimators’ inherent robustness to outliers. An illus...

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, cl...