In this paper, a powerful open Multiple Instance Learning (MIL) framework is proposed. Such an open framework is powerful since different sub-methods can be plugged into the framework to generate different specific Multiple Instance Learning algorithms. In our proposed framework, the Multiple Instance Learning problem is first converted to an unconstrained optimization problem by the Minimum Sq...

An efficient sensor scheduling is proposed� which is an interesting issue by applying wireless sensor� network (WSN) for addressing to track a mobile target� under fading channel is proposed in this report, The� proposed efficient and simple algorithm is mainly for� assisting mobile sensors management that is deployed in� tracking a considerable moving target. The presented� novel method leads ...

This module motivates and introduces the minimum variance unbiased estimator (MVUE). This is the primary criterion in the classical (frequentist) approach to parameter estimation. We introduce the concepts of mean squared error (MSE), variance, bias, unbiased estimators, and the bias-variance decomposition of the MSE. The Minimum Variance Unbiased Estimator 1 In Search of a Useful Criterion In ...

This study concerns with a trust-region-based method for solving unconstrained optimization problems. The approach takes the advantages of the compact limited memory BFGS updating formula together with an appropriate adaptive radius strategy. In our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-Newt...

Abstract For the unconstrained optimization of black box functions, this paper introduces a new randomized algorithm called . In practice, matches quality other state-of-the-art algorithms for finding, in small and large dimensions, local minimizer with reasonable accuracy. Although our theory guarantees only minimizers heuristic techniques turn into an efficient global solver. very thorough nu...