Dottorato di Ricerca in Ricerca Operativa, Ciclo XX , a.a. 2006-2007The objective of the thesis is to adopt advanced machine learning tech-
niques in the analysis of the output of sensor systems. In particular we
have focused on the SVM (Support Vector Machine) approach to classi-
¯cation and regression, and we have tailored such approach for the area
of sensor systems of the "electronic nose" type.
We designed an Electronic Nose (ENose), containing 8 sensors, 5 of
them being gas sensors, and the other 3 being a Temperature, a Humidity,
and a Pressure sensor, respectively. Our system (Electronic Nose) has the
ability to identify the type of gas, and then to estimate its concentration.
To identify the type of gas we used as classi¯cation and regression
technique the so called Support Vector Machine (SVM) approach, which
is based on statistical learning theory and has been proposed in the broad
learning ¯eld. The Kernel methods are applied in the context of SVM, to
improve the classi¯cation quality. Classi¯cation means ¯nding the best
divider (separator) between two or more di®erent classes without or with
minimum number of errors. Many methods for pattern recognition or
classi¯cation are based on neural network or other complex mathematical
models.
In this thesis we describe the hardware equipment which has been
designed and implemented. We survey the SVM approach for machine
learning and report on our experimentation.Università degli Studi della Calabri
