Abstract
In this paper, a classifier called Generalized Learning Local Averaging Classifier (GLLAC) is proposed for image classification. GLLAC is regarded as a combination of Local Averaging Classifier (LAC) and Generalized Learning Vector Quantization (GLVQ) for achieving low error rates with small amount of reference vectors. In GLLAC, all k-near reference vectors of the nearest mean vector belonging to the same class to an input vector are moved toward an input vector, whereas those of the nearest mean vector from a different class are moved away from an input vector. The performance of GLLAC is verified with experiments on handwritten digit and color image classification. Experimental results show that GLLAC can achieve lower error rates than conventional classifiers such as GLVQ or Support Vector Machine (SVM).
