Application of Machine Learning for Optimization

Abstract

This chapter reviews the literature on machine learning and presents regularly used machine learning algorithms in an optimization framework. The interaction between learning algorithm and optimization shell are scrutinized. Methodologies that increase the scalability and efficiency are discussed. Optimizations strategies are predominant in customer support analytics. Optimization schedule basically endeavours to discover the greatest or least of a job, like the objective work, by creating a calculation that methodically chooses input values from a permitted set and computes the esteem of the work. Machine learning favours less-complex calculations that work in sensible computational time. Any side from data fitting, there are various optimization problems and optimization algorithms, and machine learning can ease the solution. In addition, many methods extensively used for the analytics of customer support have been proposed in optimization problems over the last few decades to obtain optimal resolution. Pros and cons of these models and future research directions have been shown.