Interpreting the information hidden in multidimensional data can be considered as a challenging and also a complicated task. The compression, dimension reduction and visualization of these multidimensional data provide ways to better understanding and interpretation of the problem. Usually, dimension reduction or compression is considered as the first step to data analysis and exploration. Here...

High capacity associative neural networks can be built from networks of perceptrons, trained using simple perceptron training. Such networks perform much better than those trained using the standard Hopfield one shot Hebbian learning. An experimental investigation into how such networks perform when the connection weights are not free to take any value is reported. The three restrictions invest...

Auto-Associative models cover a large class of methods used in data analysis, among them are for example the famous PCA and the auto-associative neural networks. In this paper, we describe the general properties of these models when the projection component is linear and we propose and test an easy to implement Probabilistic Semi-Linear Auto-Associative model in a Gaussian setting. We show that...

Artificial Intelligence has been present since more than two decades ago, in the treatment of data concerning the protection of the environment; in particular, various groups of researchers have used genetic algorithms and artificial neural networks in the analysis of data related to the atmospheric sciences and the environment. However, in this kind of applications has been conspicuously absen...

This thesis is concerned with one important question in artificial neural networks, that is, how biologically inspired connectivity of a network affects its associative memory performance. In recent years, research on the mammalian cerebral cortex, which has the main responsibility for the associative memory function in the brains, suggests that the connectivity of this cortical network is far ...

Abstmck One promising approach to neural network controlled robotics is the use of autoassociative networks. These networks learn to move a "sensor and effector" vector through a plausible state-space. This approach is, however, hindered by the intrinsically inefficient nature of autoassociative networks. This paper outlines a novel approach that greatly increases the efficiency and resolution ...

The classification of uncertain datasets is an emerging research problem that has recently attracted significant attention. Some attempts to devise a classification model with uncertain training data have been proposed using decision trees, neural networks, or other approaches. Among those, the associative classifiers have inspired some of the uncertain classification algorithms given their pro...