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This paper deals with an application of a Fuzzy-ART self-organizing neural classiier to adaptive cate-gorization of the perceptual space of a mobile robot. The aim of the presented research is to develop a learning system for reactive locomotion control in an unknown, cluttered environment. A qualitative description of the proposed categorization technique(More)
Regularization is a principled way to control model complexity, prevent overfitting, and incorporate ancillary information into the learning process. As a convex relaxation of ℓ0-norm, ℓ1-norm regularization is popular for learning in high-dimensional spaces, where a fundamental assumption is the sparsity of model parameters. However, model sparsity can be(More)
EPFC (Emerging Patterns in Food Complaints) is the analytical component of the Consumer Complaint Monitoring System, designed to help the food safety officials to efficiently and effectively monitor incoming reports of adverse effects of food on its consumers. These reports, collected in a passive surveillance mode, contain multi-dimensional, heterogeneous(More)
In this paper, we address the question of what kind of knowledge is generally transferable from unlabeled text. We suggest and analyze the semantic correlation of words as a generally transferable structure of the language and propose a new method to learn this structure using an appropriately chosen latent variable model. This semantic correlation contains(More)
South and SouthEast Asian countries are currently in the midst of a new epidemic of Dengue Fever. This paper presents disease surveillance systems in Sri Lanka and India, monitoring a handful of com-municable diseases termed as notifiable. These systems typically require 15-30 days to communicate field data to the central Epidemiology Units, to be then(More)