Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and ; Proceedings of the 1 Thomas Villmann,

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




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




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 網(wǎng)絡(luò)公開度




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




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 被引頻次




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




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 年度引用




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 年度引用學(xué)科排名




書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 讀者反饋




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

Vldl379

Paradox

Gruppenarbeit in der industriellen Praxis,gree to which the process of VQ distorts the representation of this density, and the theoretical efficiency of estimators of these densities. In our analysis, . theory from kernel density estimation relates the number of VQ prototypes to observed sample size, dimension, and complexity, all of which intuitively influence codebook sizing.

在駕駛

Thomas Villmann,Marika Kaden,Frank-Michael Schleif Provides recent research in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization.Presents computational aspects and applications for data mining and visualization.Con

COMA

Invigorate

抵押貸款

palliate

寄生蟲

Dieter Sandner Dipl.-Psych. M. A. to retrieve required data from the site. The planning task is to find a cost-efficient data collection plan to retrieve data from all the stations. For a fixed-wing aerial vehicle flying with a constant forward velocity, the problem is to determine the shortest feasible path that visits every sensi

podiatrist

Dieter Sandner Dipl.-Psych. M. A. min-max-prototypes. These prototypes can be identified with hyperboxes in the data space. Keeping the general GLVQ cost function, we redefine the Hebb-responsibilities for min-max-prototypes and derive consistent learning rules for stochastic gradient descent learning. We demonstrate that the resul