Titlebook: Advances in Self-Organizing Maps and Learning Vector Quantization; Proceedings of the 1 Thomas Villmann,Frank-Michael Schleif,Mandy Lange C

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书目名称Advances in Self-Organizing Maps and Learning Vector Quantization影响因子(影响力)




书目名称Advances in Self-Organizing Maps and Learning Vector Quantization影响因子(影响力)学科排名




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书目名称Advances in Self-Organizing Maps and Learning Vector Quantization网络公开度学科排名




书目名称Advances in Self-Organizing Maps and Learning Vector Quantization被引频次




书目名称Advances in Self-Organizing Maps and Learning Vector Quantization被引频次学科排名




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Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Determinising data in many settings. It is shown that (ii) discriminative models provide a better classification accuracy also when combined with reject strategies based on probabilistic models as compared to generative ones.

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How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?ions in order to outline differences and similarities between them. It discuss the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications.

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User Defined Conceptual Modeling Gestures,rate this fact in a few benchmarks. Further, we investigate the behavior of the models if this objective is explicitly formalized in the mathematical costs. This way, a smooth transition of the two partially contradictory objectives, discriminative power versus model representativity, can be obtained.