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• W2586367066 abstract "Fuzzy cognitive maps (FCM) are often represented and implemented using matrix-vector multiplication (MxV). Since the multiplication operation is critical to the performance of the FCM computations, it is important to secure its efficient implementation. Considering the connection matrix used to represent the FCM is often static and since it often contains only several nonzero elements, it is viable to transform it into another particular representation suitable to perform sparse matrix-vector multiplication (SpMxV). This paper shows a performance benchmark for the most common SpMxV representations, namely the CRS and CCS. It also examines the sparsity threshold at which it is more efficient to use naive dense MxV." @default.
• W2586367066 created "2017-02-17" @default.
• W2586367066 creator A5057365557 @default.
• W2586367066 creator A5058962515 @default.
• W2586367066 creator A5090441248 @default.
• W2586367066 date "2016-10-01" @default.
• W2586367066 modified "2023-10-02" @default.
• W2586367066 title "Efficient FCM computations using sparse matrix-vector multiplication" @default.
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• W2586367066 doi "https://doi.org/10.1109/smc.2016.7844885" @default.
• W2586367066 hasPublicationYear "2016" @default.
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