NSCaching
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Machine learning has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to automatically build machine learning applications without extensive knowledge of statistics and machine learning. In this survey, we summarize the recent developments in academy and industry regarding AutoML. First, we introduce a holistic problem formulation. Next, approaches for solving various subproblems of AutoML are presented. Finally, we provide an extensive empirical evaluation of the presented approaches on synthetic and real data.
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Machine learning techniques have deeply rooted in our everyday life. How...
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Automated machine learning (AutoML) aims to find optimal machine learnin...
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Automated machine learning (AutoML) is essentially automating the proces...
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In the last few years, Automated Machine Learning (AutoML) has gained mu...
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In machine learning tasks, especially in the tasks of prediction, scient...
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Software systems trained via machine learning to automatically classify
...
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Despite incredible recent advances in machine learning, building machine...
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