DocumentCode
3285685
Title
A learning machine for resource-limited adaptive hardware
Author
Anguita, Davide ; Ghio, Alessandro ; Pischiutta, Stefano
Author_Institution
Univ. of Genoa, Genoa
fYear
2007
fDate
5-8 Aug. 2007
Firstpage
571
Lastpage
576
Abstract
Machine learning algorithms allow to create highly adaptable systems, since their functionality only depends on the features of the inputs and the coefficients found during the training stage. In this paper, we present a method for building support vector machines (SVM), characterized by integer parameters and coefficients. This method is useful to implement a pattern recognition system on resource-limited hardware, where a floating-point unit is often unavailable.
Keywords
resource allocation; support vector machines; SVM; floating-point unit; learning machine; pattern recognition system; resource-limited adaptive hardware; support vector machines; Backpropagation algorithms; Feedforward systems; Hardware; Machine learning; Machine learning algorithms; Microprocessors; Pattern recognition; Signal processing algorithms; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Adaptive Hardware and Systems, 2007. AHS 2007. Second NASA/ESA Conference on
Conference_Location
Edinburgh
Print_ISBN
978-0-7695-2866-3
Type
conf
DOI
10.1109/AHS.2007.6
Filename
4291969
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