Maximum Likelihood Topology Preserving Ensembles

Statistical re-sampling techniques have been used extensively and successfully in the machine learning approaches for generations of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the re-sampling techniques in the context of a topology preserving map which can be used for scale invariant classification, taking into account the fact that it models the residual after feedback with a family of distributions and finds filters which make the residuals most likely under this model. This model is applied to artificial data sets and compared with a similar version based on the Self Organising Map (SOM)

Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models

This multidisciplinary study focuses on the application and comparison of several topology preserving mapping models upgraded with some classifier ensemble and boosting techniques in order to improve those visualization capabilities. The aim is to test their suitability for classification purposes in the field of food industry and more in particular in the case of dry cured ham. The data is obtained from an electronic device able to emulate a sensory olfative taste of ham samples. Then the data is classified using the previously mentioned techniques in order to detect which batches have an anomalous smelt (acidity, rancidity and different type of taints) in an automated way

A Guiding Agent: smart dynamic technology for solving distributed problems.

Mobile technology is everywhere nowadays in the developed world. This technology is mature enough to support intelligent applications and smart devices. Over the last few years we have developed a number of applications for PDAs and Mobile phones. This abstract outlines an information system that incorporate a recommender agent that helps the users of a shopping centre to identify offers, to find people or to define a plan which in the shopping centre for a day. The multiagent architecture incorporates a smart deliberative agent that take decisions with the help of casebased planners. The system that uses past experiences to recommend future actions has been tested successfully