Visualization of Topology Representing Networks

Vathy-Fogarassy, Ágnes; Werner-Stark, Ágnes; Gal, Balazs; Abonyi, János

doi:10.1007/978-3-540-77226-2_57

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…We used these quantization and topological errors because we put much importance on the reproducibility of our results. Though many measures have been proposed, in particular for the topological preservation, for example, the trustworthiness and continuity [48][49][50][51][52][53][54][55], experimental results have shown that final results vary significantly, depending upon the different settings in the experiments [56].…”

Section: Resultsmentioning

confidence: 99%

Double enhancement learning for explicit internal representations: unifying self-enhancement and information enhancement to incorporate information on input variables

Kamimura

2011

Appl Intell

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In this paper, we propose a new type of information-theoretic method called "double enhancement learning," in which two types of enhancement, namely, selfenhancement and information enhancement, are unified. Self-enhancement learning has been developed to create targets spontaneously within a network, and its performance has proven to be comparable with that of conventional competitive learning and self-organizing maps. To improve the performance of the self-enhancement learning, we try to include information on input variables in the framework of self-enhancement learning. The information on input variables is computed by information enhancement in which a specific input variable is used to enhance competitive unit outputs. This information is again used to train a network with the self-enhancement learning. We applied the method to three problems, namely, an artificial data, a student survey and the voting attitude problem. In all three problems, quantization errors were significantly decreased with the double enhancement learning. The topographic errors were relatively higher, but the smallest number of topographic errors was also obtained by the double enhancement learning. In addition, we saw that U-matrices for all problems showed explicit boundaries reflecting the importance of input variables.

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