Feb. 13, 2024, 5:42 a.m. | Albert Belenguer-Llorens Carlos Sevilla-Salcedo Emilio Parrado-Hern\'andez Vanessa G\'omez-Verdejo

cs.LG updates on arXiv.org arxiv.org

This paper presents the Relevance Feature and Vector Machine (RFVM), a novel model that addresses the challenges of the fat-data problem when dealing with clinical prospective studies. The fat-data problem refers to the limitations of Machine Learning (ML) algorithms when working with databases in which the number of features is much larger than the number of samples (a common scenario in certain medical fields). To overcome such limitations, the RFVM incorporates different characteristics: (1) A Bayesian formulation which enables the …

algorithms applications challenges clinical cs.lg data databases feature features health limitations machine machine learning novel paper studies vector

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