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MCS
2002
Springer

Boosting and Classification of Electronic Nose Data

13 years 5 months ago
Boosting and Classification of Electronic Nose Data
Abstract. 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.
Francesco Masulli, Matteo Pardo, Giorgio Sbervegli
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where MCS
Authors Francesco Masulli, Matteo Pardo, Giorgio Sberveglieri, Giorgio Valentini
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