High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this paper, we are interested in a broad class of sparse learning approaches formulated as linear programs parametrized by a regularization factor, and solve them by the parametric simplex method (PSM). Our parametric simplex method offers significant advantages over other competing met...

A parametric approach is developed to the method of S-tree diagrams and its generalization for investigation of the hierarchical substructure of N-body nonlinearly interacting systems (e.g., clusters of galaxies). The introduction of a parametric function allows us to take into account the individual features of the particles which do not have a direct influence on the dynamics of the system bu...

Nowadays, one of the most important topics in risk management of banks, financial, and credit institutions is credit risk management. In this research, the researchers used survival analytic methods for credit risk modeling in terms of the conditional distribution function of default time. As a practical task, the authors considered the reward credit portfolio of Tose'e Ta'avon Bank of Guilan P...

We have developed a trainable method of shape representation which can automatically capture the invariant properties of a class of shapes and provide a compact parametric description of variability. We have applied the method to a family of flexible ribbons (worms) and to heart shapes in echocardiograms. We show that in both cases a natural parameterisation of shape results.

Shape models have been used widely to achieve robust interpretation of complex images. They allow image evidence to be organized into plausible interpretations which can then be verified”“. We are interested in the class of problems where shapes are variable. Important examples are the inspection of complex manufactured assemblies where relative motion between subparts is possible. and medical ...

We have developed a trainable method of shape representation which can automatically capture the invariant properties of a class of shapes and provide a compact parametric description of variability. We have applied the method to a family of flexible ribbons (worms) and to heart shapes in echocardiograms. We show that in both cases a natural parameterisation of shape results.