Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007) A learning machine for resource-limited adaptive hardware University of Edinburgh, Scotland, United Kingdom August 05-August 08 ISBN: 0-7695-2866-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AHS.2007.6
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 floatingpoint unit is often unavailable.
Citation:
Davide Anguita, Alessandro Ghio, Stefano Pischiutta, "A learning machine for resource-limited adaptive hardware," ahs, pp.571-576, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||