Multidimensional scaling (MDS) methods are designed to establish a one-to-one correspondence of input-output relationships. While the input may be given as high-dimensional data it...
Information visualization and visual data mining leverage the human visual system to provide insight and understanding of unorganized data. In order to scale to massive sets of hig...
We investigate when sparse coding of sensory inputs can improve performance in a classification task. For this purpose, we use a standard data set, the MNIST database of handwritte...
We explore the capability of the Self Organizing Map for structured data (SOM-SD) to compress continuous time data recorded from a kinematic tree, which can represent a robot or an...
Abstract. Ensemble methods allow to improve the accuracy of classification methods. This work considers the application of one of these methods, named Rotation-based, when the clas...
In this study, a method for hierarchical examination and visualization of GSM data using the Self-Organizing Map (SOM) is described. The data is examined in few phases. At first te...
Abstract. This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuro...
Blind source separation (BSS) has become one of the major signal and image processing area in many applications. Principal component analysis (PCA) and Independent component analys...
The training of Emergent Self-organizing Maps (ESOM ) with large datasets can be a computationally demanding task. Batch learning may be used to speed up training. It is demonstrat...
In this paper we demonstrate the generalization property of spiking neurons trained with ReSuMe method. We show in a set of experiments that the learning neuron can approximate the...