An Optimization Algorithm Based on Active and Instance-Based Learning

Abstract

Abstract. We present an optimization algorithm that combines active learning and locally-weighted regression to find extreme points of noisy and complex functions. We apply our algorithm to the problem of interferogram analysis, an important problem in optical engineering that is not solvable using traditional optimization schemes and that has received recent attention in the research community. Experimental results show that our method is faster than others previously presented in the literature and that it is very accurate for the case of noiseless interferograms, as well as for the case of interferograms with two types of noise: white noise and intensity gradients, which are due to slight missalignments in the system.

An active instance-based machine learning method for stellar population studies

“…In this paper, the first paper of a series, we test a technique that approximates non‐linear multidimensional functions using a small initial training set; and by using active learning it increases this training set as needed according to the elements of the test set. This method has shown to outperform traditional instance‐based learning algorithms on the problem of interferogram analysis (Fuentes & Solorio 2004).…”