Titlebook: Advances in Self-Organising Maps; Nigel Allinson,Hujun Yin,Jon Slack Conference proceedings 2001 Springer-Verlag London Limited 2001 Data

AMUSE

書目名稱Advances in Self-Organising Maps影響因子(影響力)




書目名稱Advances in Self-Organising Maps影響因子(影響力)學(xué)科排名




書目名稱Advances in Self-Organising Maps網(wǎng)絡(luò)公開度




書目名稱Advances in Self-Organising Maps網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Self-Organising Maps被引頻次




書目名稱Advances in Self-Organising Maps被引頻次學(xué)科排名




書目名稱Advances in Self-Organising Maps年度引用




書目名稱Advances in Self-Organising Maps年度引用學(xué)科排名




書目名稱Advances in Self-Organising Maps讀者反饋




書目名稱Advances in Self-Organising Maps讀者反饋學(xué)科排名

古董

樹木心

https://doi.org/10.1007/978-1-4471-4048-1mize the differential entropies of the kernel outputs and, at the same time, to minimize the mutual information between these outputs. The learning scheme is based on . learning supplemented with a cooperative/competitive stage to achieve topographically-organized maps. As a potential application, we consider density estimation.

sphincter

Intellectual

Iva Bojic,Tomislav Lipic,Vedran Podobnike, we suggest a new version of SOM, using the supervised learning approach. We compare the supervised version and the unsupervised version of SOM-SD on a benchmark problem involving visual patterns. As may be expected, the supervised version is able to solve the classification problem using very compact networks.

myalgia

Lecture Notes in Computer Scienceto identify health inequality structure is critical. It is believed that health inequality analysis is a complex problem. Self- organisation mapping is therefore employed in this paper to analyse health inequalities based on a data set provided by the Centre for Disease Control and Prevention, USA (CDC).

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