An ANALYSIS on VARIATIONS of INPUT PATTERN DISTRIBUTIONS in SELF-ORGANIZING MAPS in 2D
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چکیده
Self-organizing mapping is an unsupervised learning paradigm used in pattern classification and hence artificial intelligence. This paradigm is based on modifying the class features via the incoming input stimuli. Its exciting part is that it introduces concepts such as neighborhood or mapping. Hence the results obtained from this paradigm highly depend on the selected neighborhood and mapping schemas as well as the distribution of the input stimuli on the feature space. We implemented a self-organizing map simulation tool and simulated the paradigm with variations on these parameters. This paper contains the analysis of the simulations and the discussion of the results.
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