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Theoretical Understanding of Classic Learning Algorithms

CORDIS · observation · Publication date unknown

Theoretical Understanding of Classic Learning Algorithms 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 the most important such algorithms, namely Bagging and Boosting. As a result of this study, we shall provide faster learning algorithms that require less training data to make accurate predictions. The project accomplishes this by pursuing several objectives: 1. We will establish a novel learning theoretic framework for proving generalization bounds for learning algorithms. Using the framework, w

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recordType

award

status

SIGNED

region

EU

value

1999288

unit

EUR