A learning machine for resource-limited adaptive hardware

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.

• Type: Conference
• Year: 2007
• Where: AHS
• Authors: Davide Anguita, Alessandro Ghio, Stefano Pischiutta
• Keywords: Adaptable Systems, AHS 2007, Hardware, Learning Algorithms, Support Vector Machines