Standard data analysis techniques for biomedical problems cannot take into account existing prior knowledge, and available literature results cannot be incorporated in further studies. In this work we review some techniques that incorporate prior knowledge in supervised classification algorithms as constraints to the underlying optimization and linear algebra problems. We analyze a case study, to show the advantage of such techniques in terms of prediction accuracy.

Prior knowledge in the classification of biomedical data

Guarracino Mario Rosario
2010

Abstract

Standard data analysis techniques for biomedical problems cannot take into account existing prior knowledge, and available literature results cannot be incorporated in further studies. In this work we review some techniques that incorporate prior knowledge in supervised classification algorithms as constraints to the underlying optimization and linear algebra problems. We analyze a case study, to show the advantage of such techniques in terms of prediction accuracy.
2010
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
978-3-642-14746-3
Generalized Eigenvalue Classifier; Neural Networks; Supervised classification; Support Vector Machines
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/71035
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