Abstract
This paper presents a novel class of neural networks which can be trained in an unsupervised manner to detect a mixture of hyperellipsoidal shells and/or segments of hyperellipsoidal shells. This approach is computationally and implementationally simpler than other clustering algorithms that have been suggested for this purpose. Experimental results on several data sets are presented.
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Su, MC., Liu, IC. A Neural-Network-Based Approach to Detecting Hyperellipsoidal Shells. Neural Processing Letters 9, 279–292 (1999). https://doi.org/10.1023/A:1018676508833
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DOI: https://doi.org/10.1023/A:1018676508833