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Representation of spaces including both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Therefore, in recent years identifying and semantically labeling the environments based on onboard sensors has become an important competency for mobile robots. Supervised learning algorithms have been extensively used for this purpose with SVM-based solutions...
Extending metric space representations of an environment with other high level information, such as semantic and topological representations enable a robotic device to efficiently operate in complex environments. This paper proposes a methodology for a robot to classify indoor environments into semantic categories. Classification task, using data collected from a laser range finder, is achieved by...
There are many techniques for electricity market price forecasting. The challenge of spike prediction is the accuracy of the prediction that is on how a classifier can capture all spikes that would happen. In this paper, we introduce a novel data discretization approach using cloud models to implement concept hierarchies and data reduction. An effective framework of predicting the occurrence of spikes...
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