Abstract
This paper describes the design of a digital architecture suitable for the classification of large quantities of measurement data by means of a method based on the Support Vector Machines (SVMs). The proposed approach can be applied for solving general inverse modeling problems and for processing complex measurement data requiring real-time processing, possibly in a distributed mode over a number of physically small and geographically separated ‘computational nodes’. A problem of nonlinear channel equalization and a classification task from high energy physics are presented as discussed as two case studies for which the ability of achieving real-time processing is of paramount importance. The performance of such architectures is then analyzed in terms of its speed of execution, occupancy of the hardware modules available in a Virtex II FPGA chip, and classification error.
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Boni, A., Lazzizzera, I., Zorat, A. (2005). Neural Hardware Based on Kernel Methods for Industrial and Scientific Applications. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_14
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DOI: https://doi.org/10.1007/1-4020-3432-6_14
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
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