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
Inglese
Combining Soft Computing and Statistical Methods in Data Analysis
1st International Workshop on Soft Methods in Probability and Statistics (SMPS 2002), WARSAW, POLAND, September 2002
1
8
978-3-642-14746-3
Springer-Verlag
Berlin Heidelberg
GERMANIA
Sì, ma tipo non specificato
2002
WARSAW
Generalized Eigenvalue Classifier; Neural Networks; Supervised classification; Support Vector Machines
1
none
Abbate, Danilo ; De Asmundis, Roberta ; Guarracino, Mario Rosario
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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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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