Search results for " Regular"

A Riemann manifold structure of the spectra of weighted algebras of holomorphic functions

2009

[EN] In this paper we give general conditions on a countable family V of weights on an unbounded open set U in a complex Banach space X such that the weighted space HV (U) of holomorphic functions on U has a Frechet algebra structure. For such weights it is shown that the spectrum of HV(U) has a natural analytic manifold structure when X is a symmetrically regular Banach space, and in particular when X = C-n. (C) 2009 Elsevier Ltd. All rights reserved.

Groups whose prime graph on conjugacy class sizes has few complete vertices

2012

Abstract Let G be a finite group, and let Γ ( G ) denote the prime graph built on the set of conjugacy class sizes of G. In this paper, we consider the situation when Γ ( G ) has “few complete vertices”, and our aim is to investigate the influence of this property on the group structure of G. More precisely, assuming that there exists at most one vertex of Γ ( G ) that is adjacent to all the other vertices, we show that G is solvable with Fitting height at most 3 (the bound being the best possible). Moreover, if Γ ( G ) has no complete vertices, then G is a semidirect product of two abelian groups having coprime orders. Finally, we completely characterize the case when Γ ( G ) is a regular …

General measure theory

1995

Maximal regularity for Kolmogorov operators in L2 spaces with respect to invariant measures

2006

Abstract We prove an optimal embedding result for the domains of Kolmogorov (or degenerate hypoelliptic Ornstein–Uhlenbeck) operators in L 2 spaces with respect to invariant measures. We use an interpolation method together with optimal L 2 estimates for the space derivatives of T ( t ) f near t = 0 , where T ( t ) is the Ornstein–Uhlenbeck semigroup and f is any function in L 2 .

A dual of 4-regular graph forG × C2n

2003

Abstract A graph is said h-decomposable if its edge-set is decomposable into edge-disjoint hamiltonian cycles. Jha [3] conjectured that if G is a non-bipartite h-decomposable graph on even number of vertices, then G × K2 is h-decomposable. We use the notion of dual graph defined in [4], we prove that if G = Q1,2 ⊕ C3,4 is a 4-regular non-bipartite h-decomposable graph and the dual graphs relative to Q1,2 and C3,4 are connected then G × K 2 and G × C 2n are h-decomposable (where C 2n is an even cycle).

Regularized extreme learning machine for regression problems

2011

Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated. Empirical studies…

Prediction of type 2 diabetes mellitus based on nutrition data

2021

Abstract Numerous predictive models for the risk of type 2 diabetes mellitus (T2DM) exist, but a minority of them has implemented nutrition data so far, even though the significant effect of nutrition on the pathogenesis, prevention and management of T2DM has been established. Thus, in the present study, we aimed to build a predictive model for the risk of T2DM that incorporates nutrition data and calculates its predictive performance. We analysed cross-sectional data from 1591 individuals from the population-based Cooperative Health Research in the Region of Augsburg (KORA) FF4 study (2013–14) and used a bootstrap enhanced elastic net penalised multivariate regression method in order to bu…

An entropy-based machine learning algorithm for combining macroeconomic forecasts

2019

This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.

A machine learning application to predict early lung involvement in scleroderma: A feasibility evaluation

2021

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations

Scad-elastic net and the estimation of individual tourism expenditure determinants

2014

This paper introduces the use of scad-elastic net in the assessment of the determinants of individual tourist spending. This technique approaches two main estimation-related issues of primary importance. So far studies of tourism literature have made a wide use of classic regressions, whose results might be affected by multicollinearity. In addition, because of the absence of robust economic theory on tourism behavior, regressor selection is often left to researcher's choice when not driven by non-optimal automatic criteria. Scad-elastic net is an OLS model that accounts for both these problems by including two types of parameters constraints, namely the smoothly clipped absolute deviation …