3,059 research outputs found
A Real-Time Novelty Detector for a Mobile Robot
Recognising new or unusual features of an environment is an ability which is
potentially very useful to a robot. This paper demonstrates an algorithm which
achieves this task by learning an internal representation of `normality' from
sonar scans taken as a robot explores the environment. This model of the
environment is used to evaluate the novelty of each sonar scan presented to it
with relation to the model. Stimuli which have not been seen before, and
therefore have more novelty, are highlighted by the filter. The filter has the
ability to forget about features which have been learned, so that stimuli which
are seen only rarely recover their response over time. A number of robot
experiments are presented which demonstrate the operation of the filter.Comment: 8 pages, 6 figures. In Proceedings of EUREL European Advanced
Robotics Systems Masterclass and Conference, 200
Novelty Detection for Robot Neotaxis
The ability of a robot to detect and respond to changes in its environment is
potentially very useful, as it draws attention to new and potentially important
features. We describe an algorithm for learning to filter out previously
experienced stimuli to allow further concentration on novel features. The
algorithm uses a model of habituation, a biological process which causes a
decrement in response with repeated presentation. Experiments with a mobile
robot are presented in which the robot detects the most novel stimulus and
turns towards it (`neotaxis').Comment: 7 pages, 5 figures. In Proceedings of the Second International
Conference on Neural Computation, 200
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
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