Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization; Dedicated to the Mem Jan Faigl,Madalina

culinary

書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization影響因子(影響力)




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization影響因子(影響力)學(xué)科排名




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書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization被引頻次




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization被引頻次學(xué)科排名




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書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization讀者反饋




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization讀者反饋學(xué)科排名

SLUMP

Curmudgeon

compel

巨頭

https://doi.org/10.1007/978-1-4419-1123-0of empirical inference: the hierarchical agglomerative clustering and the computation of minimum enclosing balls. It produces .-nets whose cardinalities are smaller than those obtained with state-of-the-art methods.

recede

wangle

Modification of the Classification-by-Component Predictor Using Dempster-Shafer-Theory,Dempster-Shafer-theory, which in the original approach was mentioned to be implicitly realized but not explained deeply. Thus, we redefine the CbC keeping the main aspects of positive and negative reasoning about detected components/features and relate this to the Demspster-Shafer-theory of evidence.

bizarre

,Inferring ,-nets of?Finite Sets in?a?RKHS,of empirical inference: the hierarchical agglomerative clustering and the computation of minimum enclosing balls. It produces .-nets whose cardinalities are smaller than those obtained with state-of-the-art methods.

錯(cuò)誤

,Steps Forward to?Quantum Learning Vector Quantization for?Classification Learning on?a?Theoretical t quantum computing patterns and quantum hardware. For this purpose, we introduce a new computing pattern for prototype updates and possible measurement strategies in the quantum computing regime. Further, we consider numerical errors which are induced by the theoretical model and their impact on the learning process.

高貴領(lǐng)導(dǎo)

Jan Faigl,Madalina Olteanu,Jan DrchalProvides recent research in self-organizing maps, learning vector quantization, clustering, and data visualization.Presents computational aspects and applications for data mining and visualization.Con