It is well known that our prior knowledge and experiences affect how we learn new concepts. Although several formal modeling attempts have been made to quantitatively describe the ...
Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detect...
Paulo Cortez, Miguel Rio, Pedro Sousa, Miguel Roch...
We present an artificial neural network used to learn online complex temporal sequences of gestures to a robot. The system is based on a simple temporal sequences learning architec...
Abstract. Trained support vector machines (SVMs) have a slow runtime classification speed if the classification problem is noisy and the sample data set is large. Approximating the...
This paper presents a novel system that performs text-independent speaker authentication using new spiking neural network (SNN) architectures. Each speaker is represented by a set ...
Simei Gomes Wysoski, Lubica Benuskova, Nikola Kasa...
In this paper, we discuss fuzzy classifiers based on Kernel Discriminant Analysis (KDA) for two-class problems. In our method, first we employ KDA to the given training data and ca...
Abstract. In this paper we consider the question of whether it is possible to classify n-back EEG data into different memory loads across subjects. To capture relevant information ...
Artificial neural networks are a key tool for researchers attempting to understand and replicate the behaviour and intelligence found in biological neural networks. Software simul...
A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems tha...
Abstract. In this article we present EANT2, a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation ope...