Title :
A learning machine for resource-limited adaptive hardware
Author :
Anguita, Davide ; Ghio, Alessandro ; Pischiutta, Stefano
Author_Institution :
Univ. of Genoa, Genoa
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;
Conference_Titel :
Adaptive Hardware and Systems, 2007. AHS 2007. Second NASA/ESA Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-7695-2866-3
