We examine the properties of function approximation using polynomial rules in a fuzzy system. We show that this kind of fuzzy function approximation is equivalent to Lagrange polynomial interpolation between turning points when normalized fuzzy function memberships are used. The fuzzy inference procedure combines two polynomials of degree n and m in x into one single polynomial of at most degre...

Finding maximum a posterior (MAP) estimation is common problem in computer vision, such as the inference in Markov random fields. However, it is in general intractable, and one has to resort to approximate solutions, e.g. quadratic programming. In this paper, we propose a robust Frank-Wolfe method [6] to do the MAP inference. Our algorithm optimizes the quadratic programming problem by alternat...

In this paper, a new method for gray-scale image and color zooming algorithm based on their local information is offered. In the proposed method, the unknown values of the new pixels on the image are computed by Moving Least Square (MLS) approximation based on both the quadratic spline and Gaussian-type weight functions. The numerical results showed that this method is more preferable to biline...

The softmax link is used in many probabilistic model dealing with both discrete and continuous data. However, efficient Bayesian inference for this type of model is still an open problem due to the lack of efficient upper bound for the sum of exponentials. We propose three different bounds for this function and study their approximation properties. We give a direct application to the Bayesian t...

The development of accurate models and efficient algorithms for the analysis of multivariate categorical data are important and longstanding problems in machine learning and computational statistics. In this paper, we focus on modeling categorical data using Latent Gaussian Models (LGMs). We propose a novel stick-breaking likelihood function for categorical LGMs that exploits accurate linear an...

Wedescribe a polynomial-size conic quadratic reformulation for amachine-job assignment problemwith separable convex cost. Because the conic strengthening is based only on the objective of the problem, it can also be applied to other problems with similar cost functions. Computational results demonstrate the effectiveness of the conic reformulation. © 2009 Elsevier B.V. All rights reserved.

— For given positive integers a, b, q we investigate the density of solutions (x, y) ∈ Z to congruences ax+ by ≡ 0 mod q.

Quadratic programming (QP) is an optimization problem wherein one minimizes (or maximizes) a quadratic function of a finite number of decision variable subject to a finite number of linear inequality and/ or equality constraints. In this paper, a quadratic programming problem (FFQP) is considered in which all cost coefficients, constraints coefficients, and right hand side are characterized by ...