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The TUCLA project focuses on enhancing classic machine learning algorithms, specifically Bagging and Boosting, to improve their efficiency with limited data. By establishing a new theoretical framework, it aims to create faster algorithms that require less training data for accurate predictions.
Machine learning has evolved from being a relatively isolated discipline to have a disruptive influence on all areas of science, industry and society.
Learning algorithms are typically classified into either deep learning or classic learning, where deep learning excels when data and computing resources are abundant, whereas classic algorithms shine when data is scarce.
In the TUCLA project, we expand our theoretical understanding of classic machine learning, with a particular emphasis on two of…
AARHUS UNIVERSITET
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