Related papers: Automated Machine Learning -- a brief review at the end of the early years

Automated Machine Learning -- a brief review at the end of the early years

URL: http://arxiv.org/abs/2008.08516v3
Date: Mon, 24 Aug 2020 14:45:40 GMT
Title: Automated Machine Learning -- a brief review at the end of the early years
Authors: Hugo Jair Escalante
Abstract summary: 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.
Score: 14.211962590104111
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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