Матричний метод опорних векторів з адаптивним комбінованим навчанням активаційної функції в рамках задачі розпізнавання образів

Abstract

In the article, we introduce an image recognition system that is based on least squares support vector machines with matrix inputs. Its distinctive features include not just the ability to process images in their initial matrix form without vectorization, but also that centers of activation functions are formed with the observations from the training set. The tuning procedure of the system is characterized by the combination of the supervised learning paradigm, «lazy» learning using the «neurons at data points» concept, T. Kohonen’s self-learning, and learning vector quantization. Experimental results confirm the presented theoretical analysis