Feed-Forward Support Vector Machine Without Multipliers

  • Authors:
  • D. Anguita;S. Pischiutta;S. Ridella;D. Sterpi

  • Affiliations:
  • Dept. of Biophys. & Electron. Eng., Genoa Univ.;-;-;-

  • Venue:
  • IEEE Transactions on Neural Networks
  • Year:
  • 2006

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Abstract

In this letter, we propose a coordinate rotation digital computer (CORDIC)-like algorithm for computing the feed-forward phase of a support vector machine (SVM) in fixed-point arithmetic, using only shift and add operations and avoiding resource-consuming multiplications. This result is obtained thanks to a hardware-friendly kernel, which greatly simplifies the SVM feed-forward phase computation and, at the same time, maintains good classification performance respect to the conventional Gaussian kernel