首页|New Support Vector Machines Study Findings Have Been Reported from Nile Universi ty (Support Vector Machine reconfigurable hardware implementation on FPGA)
New Support Vector Machines Study Findings Have Been Reported from Nile Universi ty (Support Vector Machine reconfigurable hardware implementation on FPGA)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on have been presented. According t o news originating from Giza, Egypt, by NewsRx editors, the research stated, "Su pport Vector Machine (SVM) is a robust Machine Learning (ML) algorithm used exte nsively in classification tasks." The news editors obtained a quote from the research from Nile University: "This work proposes a reconfigurable hardware implementation of the SVM classification algorithm for the linear and three kernel cases on FPGA. Efficient implementati ons of two generalization techniques, One-versus-All (OvA) and One-versus-One (O vO), to deal with multi-class problems are also realized on FPGA to overcome the binary nature of the SVM algorithm. The presented model is fully reconfigurable and can easily be adapted to any dataset with any number of classes or features . The results show that the proposed model excels in power efficiency, requires low area utilization, and reaches high performance up to 250.7 MHz. The two real ized generalization methods, OvO and OvA, offer a trade-off between accuracy and hardware cost. OvA provides lower accuracy than OvO and is more affected by the data imbalance problem, which becomes more dominant as the number of classes in creases; however, it is more resource-efficient than OvO."
Nile UniversityGizaEgyptAfricaAl gorithmsEmerging TechnologiesMachine LearningSupport Vector MachinesVect or Machines
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