Classification of incomplete feature vectors by radial basis function networks

The paper describes the use of radial basis function neural networks with Gaussian basis functions to classify incomplete feature vectors. The method uses the fact that any marginal distribution of a Gaussian distribution can be determined from the mean vector and covariance matrix of the joint distribution.

Community response in disasters: an ecological learning framework

Natural disasters are frequently exacerbated by anthropogenic mechanisms and have social and political consequences for communities. The role of community learning in disasters is seen to be increasingly important. However, the ways in which such learning unfolds in a disaster can differ substantially from case to case. This article uses a comparative case study methodology to examine catastrophes and major disasters from five countries (Japan, New Zealand, UK, US and Germany) to consider how community learning and adaptation occurs. An ecological model of learning is considered, where community learning is of small loop (adaptive, incremental, experimental) type or large loop (paradigm changing) type. Using this model we consider that there are three types of community learning that occur in disasters (navigation, organisation, reframing). The type of community learning that actually develops in a disaster depends upon a range of social factors such as stress and trauma, civic innovation and coercion.