Abstract
The classification through specific characteristics is of great relevance for development of machine intelligence systems in order to improve decision making capability. Spite of the existence of several classifier algorithms, they all have its drawbacks and might not be suitable for a certain application. Within the supervised learning classifiers, the definition of some parameters is a common issue, such as the learning step, number of hidden neurons in a multilayer perceptron, the margin length for the support vector machines, the overfitting in nonlinear classifiers, among other situations that in some way depend on a subjective decision. In order to overcome these issues, it is proposed in the present work a novel approach for a supervised classifier, which is capable of working with linear and nonlinear situations by calculating a hyperplane based on electric field intensity. To validate the method's performance, a well-known classification problem found in pattern recognition literature is tested and compared with another classical method. The obtained results have proven its good performance.
