Model Selection for Power Efficient Analysis of Measurement Data

Author

Marconato, A. ; Boni, A. ; Caprile, B. ; Petri, D.

Author_Institution

Dipt. di Informatica e Telecomunicazioni, Universita degli Studi di Trento

fYear

2006

fDate

24-27 April 2006

Firstpage

1524

Lastpage

1529

Abstract

In this work a novel analysis methodology of SVMs optimal solutions is presented. Such a methodology is based on a multiobjective optimization algorithm which exploits a genetic search paradigm. The application field is the design of smart microsensors, where both classification performance and complexity criteria have to be considered in order to balance accuracy and power consumption requirements

Keywords

genetic algorithms; intelligent sensors; measurement theory; microsensors; support vector machines; genetic programming; measurement data; multiobjective optimization algorithm; power efficient analysis; smart microsensors; support vector machines; Computer aided manufacturing; Costs; Data analysis; Energy consumption; Genetic algorithms; Instrumentation and measurement; Intelligent sensors; Power measurement; Support vector machine classification; Support vector machines; Support Vector Machines (SVMs); genetic programming; model selection; smart sensors;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2006. IMTC 2006. Proceedings of the IEEE

Conference_Location

Sorrento

ISSN

1091-5281

Print_ISBN

0-7803-9359-7

Electronic_ISBN

1091-5281

Type

conf

DOI

10.1109/IMTC.2006.328652

Filename

4124600