Heteroscedastic Gaussian kernel-based topographic maps (特集 生体情報解析と複雑系科学)

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概要

• 論文の詳細を見る

• Several learning algorithms for topographic map formation have been introduced that adopt overlapping activation regions, rather than Voronoiregions, usually in the form of kernel functions. We review and introduce a number of fixed point rules for training homogeneous, heteroscedastic but otherwise radially-symmetric Gaussian kernel-based topographic maps, or kernel topographic maps. We compare their performance for clustering a number of real world data sets.

• 日本知能情報ファジィ学会の論文
• 2007-12-15

著者

• Van Hulle

  K. U. Leuven Laboratorium Voor Neuro-en Psychofysiologie

• Van Hulle

  K. U. Leuven

関連論文

• Faithful representations with topographic maps
• Topology-preserving Map Formation Achieved with a Purely Local Unsupervised Competitive Learning Rule
• Heteroscedastic Gaussian kernel-based topographic maps (特集 生体情報解析と複雑系科学)

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