Luciano D. S. Pacifico

Learn More
Clustering methods aims to organize a set of items into clusters such that items within a given cluster have a high degree of similarity, while items belonging to different clusters have a high degree of dissimilarity. The self-organizing map (SOM) introduced by Kohonen is an unsupervised competitive learning neural network method which has both clustering(More)
Extreme Learning Machine (ELM) is a new learning method for single-hidden layer feedforward neural network (SLFN) training. ELM approach increases the learning speed by means of randomly generating input weights and biases for hidden nodes rather than tuning network parameters, making this approach much faster than traditional gradient-based ones. However,(More)
Extreme Learning Machine (ELM) is a learning method for single-hidden layer feedforward neural network (SLFN) training. ELM approach increases the learning speed by means of randomly generating input weights and biases for hidden nodes rather than tuning network parameters, making this approach much faster than traditional gradient-based one. In this paper,(More)
Clustering analysis aims to distribute a dataset in-groups in such a way that individuals from the same group have a high degree of similarity among each other, while individuals from different groups have a high degree of dissimilarity among each other. Clustering analysis has become an important mechanism for data exploration and understanding.(More)
Evolutionary Computing (EC) approaches have been widely applied on optimization problems, given their flexibility and capabilities to deal with difficult environments. In this context, Group Search Optimizer (GSO) was proposed as a nature-inspired algorithm based on animal searching Behaviour and group living theory to solve continuous optimization(More)