Enhanced word clustering for hierarchical text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Kullback-Leibler Distance in Determining the Classes for the Heart Sound Signal Classification
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
World modeling for autonomous systems
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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Modern autonomous robots are performing complex tasks in a real dynamic environment. This requires real-time reactive and pro-active handling of arising situations. A basis for such situation awareness and handling can be a world modeling subsystem that acquires information from sensors, fuses it into existing world description and delivers the required information to all other robot subsystems. Since sensory information is affected by uncertainty and lacks for semantic meaning, the employment of a predefined information, that contains concepts and descriptions of the surrounding world, is crucial. This employment implies matching of the world model information to prior knowledge and subsequent complementing of the dynamic descriptions with semantic meaning and missing attributes. The following contribution describes a matching mechanism based on the Kullback-Leibler and Tanimoto distances and direct assignment of the prior knowledge for the model complementation.