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Boosting and classification of electronic nose data

SBERVEGLIERI, Giorgio;
2002-01-01
2002
Nessuno
Multiple Classifier Systems
PE4_5 Analytical chemistry
WOS:000180068000026
2-s2.0-84890512686
Sì, ma tipo non specificato
Inglese
Internazionale
2364
262
271
9
Boosting methods are known to improve generalization performances of learning algorithms reducing both bias and variance or enlarging the margin of the resulting multi-classifier system. In this contribution we applied Adaboost to the discrimination of different types of coffee using data produced with an Electronic Nose. Two groups of coffees (blends and monovarieties), consisting of seven classes each, have been analyzed. The boosted ensemble of Multi-Layer Perceptrons was able to halve the classification error for the blends data and to diminish it from 21% to 18% for the more difficult monovarieties data set.
2 Contributo in Volume::2.1 Contributo in volume (Capitolo o Saggio)
4
268
none
F., Masulli; M., Pardo; Sberveglieri, Giorgio; G., Valentini
info:eu-repo/semantics/bookPart
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/34099
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