A supervised classifier scheme based on clustering algorithms
Andres Hernandez-Matamoros, Enrique Escamilla-Hernández, Karina Ruby Perez Daniel, Mariko Nakano-Miyatake, Hector Manuel Perez-Meana · 2014
This paper proposes a new classifier scheme based on classical clustering algorithms, such as the Batchelor & Wilkins y K-means algorithms which are trained in a similar form that the artificial neural network (ANN) or support vector machines (SVM). Proposed scheme has the advantage that if a new class is added, it is not necessary to train he classifier completely, but only add a new class. Experimental results show that the proposed scheme provides classification rates quite similar to those provided by the SVM with much less computational complexity.