A New Generalized Learning Vector Quantization Classifier Algorithm with Sliding-mode Optimized Training

A new Learning Vector Quantization classifier is proposed. The algorithm relies on a new training scheme for labeled sample vectors in feature space. Since weight or prototype vectors are conditioned to a well-known sliding-mode approach with use of a cost function to be minimized in terms of weight updates, new algorithm is called Optimized Generalized Learning Vector Quantization (OGLVQ). Consequently, weights are then associated to the proximity measure employed by conventional Generalized Learning Vector Quantization. New algorithm and some well-known predecessors are designed and tested for comparison with synthetic and publicly available datasets. From the experimental results, it is observed that the new classifier achieves faster training and is more successful and robust in generalizing labeled test samples picked from datasets studied than the counterparts it is compared to.