The proximal problem for structured penalties obtained via convex relaxations of submodular functions is known to be equivalent to minimizing separable convex functions over the corresponding submodular polyhedra. In this paper, we reveal a comprehensive class of structured penalties for which penalties this problem can be solved via an efficiently solvable class of parametric maxflow optimizat...

Parametric estimation of cause-specific hazard functions in a competing risks model is considered. An approximate likelihood procedure for estimating parameters of cause-specific hazard functions based on competing risks data subject to right censoring is proposed. In an assumed parametric model that may have been misspecified, an estimator of a parameter is said to be consistent if it converge...

Robustness of cellular functions is a key property of living organisms. Modelling and analysis of the genetic and biochemical networks underlying specific functions will enable quantification of the robustness as well as identification of the specific mechanisms providing robustness. Studies on cellular robustness has so far largely focused on parametric sensitivities, i.e., robustness of funct...

Based on nonlinear parametric fuzzy systems, an adaptive backstepping controller is proposed for a class of strict-feedback nonlinear systems. The nonlinear parametric fuzzy systems are capable of automatically learning their membership functions and tuning their weightings. Since the adjustable parameters of the membership functions nonlinearly appear in the fuzzy systems, the adaptive laws ar...

We propose a methodology of speech segmentation in which the LSF feature vector matrix of a segment is reconstructed optimally using a set of parametric/non-parametric functions. We have explored approximations using basis functions or polynomials. We have analyzed the performance of these methods w.r.t. phoneme segmentation (on 100 TIMIT sentences) and reconstruction error based on spectral di...

The present paper deals with some geometric classes of analytic functions, such as starlike, convex and bounded turning property in a complex domain. These classes are defined by a new linear operator in the normalized space of analytic functions. The linear operator is introduced by a convolution of Szego function involving parametric coefficients type Laguerre polynomial, with the normalized ...

This paper presents a framework for dimensioning policing functions based on parametric stationary models of VBR video traffic carried over ATM networks. Parametric models for wide sense stationary processes such as AR and ARMA have been used to accurately model VBR video sources but have been limited to simulation studies because analytical studies of such queueing models are not tractable. Th...

Supervised learning is a broad field that encompasses a number of methods, which can generally be classificed into two categories: parametric and nonparametric. In the parametric methods, it is assumed that the forms of the underlying density functions are known. The problem of estimating unknown functions can be reduced to estimating some values of parameters. In contrast, in the nonparametric...

A novel approach to fuzzy modeling based on the tuning of parametric conjunction operations is proposed. First, some novel methods for the construction of parametric generalized conjunction operations simpler than the known parametric classes of conjunctions are considered and discussed. Second, several examples of function approximation by fuzzy models, based on the tuning of the parameters of...

Generalized Bernstein-like functions (gB-like functions) with different shape parameters are used in this work. Parametric and geometric conditions generalized form developed. Some numerical examples of the parametric continuity (PC) (GC) constraints Bézier-like curves curves) analyzed graphical representation. symmetric rotation surfaces constructed by gB-like curves. Vase Capsule Taurus model...