The concept of semantic and context aware intelligent systems provides a vision for the Information Society where the emphasis lays on computing applications that can sense context from the people and the environment and wrap that knowledge into adaptable behavior. In this framework the proper and automatic classification of data gathered by sensors is of major importance. Our approach describes a model that operates as a self-evaluating classifier using on-line re-clustering, addressing adequately the basic issues of modern demands. The novelty of the model lies in a flexible and efficient initialization technique that first partitions the data space utilizing Gaussian distributions and then merges clusters so as to produce an effective partitioning. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Pertselakis, M., Frossyniotis, D., & Stafylopatis, A. (2003). An adaptable Gaussian neuro-fuzzy classifier. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 925–932. https://doi.org/10.1007/3-540-44989-2_110
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