Название: Evolutionary Learning: Advances in Theories and Algorithms Автор: Zhi-Hua Zhou, Yang Yu, Chao Qian
Издательство: Springer
Год: 2019
Страниц: 361
Язык: английский
Формат: pdf (true), djvu
Размер: 10.1 MB
Many Machine Learning (ML) tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Machine Learning is a central topic of Artificial Intelligence (AI). It aims at learning generalizable models from data, with which the system performance can be improved. As nowadays data can be continually acquired and accumulated in numerous applications such as image recognition and commodity recommendation, machine learning has been playing an increasingly important role in the successes of these applications.
