Title :
Uncertainty analysis of Learning-from-Examples algorithms
Author :
Gubian, M. ; Petri, D.
Author_Institution :
Dept. of Inf. & Commun. Technol., Univ. of Trento, Trento
Abstract :
Learning-from-Examples (LfE) algorithms are becoming popular building blocks for some type of measurement systems, like smart sensors. They enhance and extend the measurement capabilities of sensors allowing the use of sophisticated algorithms for sensor compensation, or for the automatic classification of physical phenomena. Machine learning systems differ quite a bit from components more commonly found in measurement systems, both in the way they introduce uncertainty and in the way that uncertainty is usually estimated. In this work we provide an analysis of uncertainty of such kind of systems, focusing on the peculiarities resulting from the presence of the LfE module in the measurement chain. The analysis is at a theoretical level, with support of numerical simulations.
Keywords :
computerised instrumentation; intelligent sensors; learning (artificial intelligence); measurement systems; measurement uncertainty; support vector machines; Learning-from-Examples algorithm; automatic classification; machine learning systems; measurement systems; measurement uncertainty; neural networks; physical phenomena; sensor compensation; smart sensors; support vector machines; uncertainty analysis; Algorithm design and analysis; Biology computing; Biosensors; Intelligent sensors; Learning systems; Machine learning; Machine learning algorithms; Neural networks; Sensor phenomena and characterization; Uncertainty; neural networks; smart sensors; support vector machines; uncertainty sources;
Conference_Titel :
Advanced Methods for Uncertainty Estimation in Measurement, 2008. AMUEM 2008. IEEE International Workshop on
Conference_Location :
Trento
Print_ISBN :
978-1-4244-2236-4
Electronic_ISBN :
978-1-4244-2237-1
DOI :
10.1109/AMUEM.2008.4589931