论文标题
自动化机器学习 - 早年结束时进行了简短的审查
Automated Machine Learning -- a brief review at the end of the early years
论文作者
论文摘要
自动化机器学习（AUTOML）是机器学习的子场，旨在在某种程度上自动化机器学习系统设计的所有阶段。在监督学习的背景下，AutoML关注特征提取，预处理，模型设计和后处理。在最近的十年中，汽车的主要贡献和成就已经发生。因此，我们正处于完美的时机之中，可以回头并意识到我们学到的知识。本章旨在总结汽车早期的主要发现。更具体地说，在本章中，提供了监督学习的Automl简介，并介绍了该领域进度的历史回顾。同样，描述了汽车的主要范例，并概述了研究机会。
Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the context of supervised learning, AutoML is concerned with feature extraction, pre processing, model design and post processing. Major contributions and achievements in AutoML have been taking place during the recent decade. We are therefore in perfect timing to look back and realize what we have learned. This chapter aims to summarize the main findings in the early years of AutoML. More specifically, in this chapter an introduction to AutoML for supervised learning is provided and an historical review of progress in this field is presented. Likewise, the main paradigms of AutoML are described and research opportunities are outlined.
