  Machine learning algorithms deployed on edge devices must meet certain
resource constraints and efficiency requirements. Random Vector Functional Link
(RVFL) networks are favored for such applications due to their simple design
and training efficiency. We propose a modified RVFL network that avoids
computationally expensive matrix operations during training, thus expanding the
network's range of potential applications. Our modification replaces the
least-squares classifier with the Generalized Learning Vector Quantization
(GLVQ) classifier, which only employs simple vector and distance calculations.
The GLVQ classifier can also be considered an improvement upon certain
classification algorithms popularly used in the area of Hyperdimensional
Computing. The proposed approach achieved state-of-the-art accuracy on a
collection of datasets from the UCI Machine Learning Repository - higher than
previously proposed RVFL networks. We further demonstrate that our approach
still achieves high accuracy while severely limited in training iterations
(using on average only 21% of the least-squares classifier computational
costs).
