Neural-style microsystems that learn

The basic operation of biological and electronic (artificial) neural networks (NNs) is examined. Learning by NNs is discussed, covering supervised learning, particularly back-propagation, and unsupervised and reinforcement learning. The use of VLSI implementation to speed learning is considered briefly. Applications of neural-style learning chips to pattern recognition, data compression, optimization, and expert systems is discussed. Problem areas and issues for further research are addressed     

Learning of stable states in stochastic asymmetric networks

Boltzmann-based models with asymmetric connections are investigated. Although they are initially unstable, these networks spontaneously self-stabilize as a result of learning. Moreover, pairs of weights symmetrize during learning; however, the symmetry is not enough to account for the observed stability. To characterize the system it is useful to consider how its entropy is affected by learning and the entropy of the information stream. The stability of an asymmetric network is confirmed with an electronic model.     

Feature-based and Clique-based User Models for Movie Selection: A Comparative Study

The huge amount of information available in the currently evolving world wide information infrastructure at any one time can easily overwhelm end-users. One way to address the information explosion is to use an ‘information filtering agent’ which can select information according to the interest and/or need of an end-user. However, at present few information filtering agents exist for the evolving world wide multimedia information infrastructure. In this study, we evaluate the use of feature-based approaches to user modeling with the purpose of creating a filtering agent for the video-on-demand application. We evaluate several feature and clique-based models for 10 voluntary subjects who provided ratings for the movies. Our preliminary results suggest that feature-based selection can be a useful tool to recommend movies according to the taste of the user and can be as effective as a movie rating expert. We compare our feature-based approach with a clique-based approach, which has advantages where information from other users is available. This revised version was published online in July 2006 with corrections to the Cover Date.