In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for t...
In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, w...
Assem Kaylani, Michael Georgiopoulos, Mansooreh Mo...
Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have be...
Christian Wallraven, Barbara Caputo, Arnulf B. A. ...
In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. In particular a ne...
Service robots need object recognition strategy that can work on various objects in complex backgrounds. Since no single method can work in every situation, we need to combine sev...