Titlebook: Advances in Self-Organizing Maps; 8th International Wo Jorma Laaksonen,Timo Honkela Conference proceedings 2011 Springer-Verlag GmbH Berlin

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




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




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




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




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




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

罐里有戒指

Contextually Self-Organized Maps of Chinese Wordse SOM, in this work histograms of various word classes or otherwise defined subsets of words were formed on the SOM array. It was found that the words are not only clustered according to the word classes, but joint or overlapping clusters of words from different classes can also be formed according

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Assessing the Efficiency of Health Care Providers: A SOM Perspectiveused as input the data from the balance sheets of 300 health care providers, as resulting from the Italian Statistics Institute (ISTAT) database, and we examined their representation obtained both by running classical SOM algorithm, and by modifying it through the replacement of standard Euclidean d

笨拙的我

Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Seriess a stand-alone clustering technique, its output has also been used as input for second-stage clustering. However, one ambiguity with the SOM clustering is that the degree of membership in a particular cluster is not always easy to judge. To this end, we propose a fuzzy C-means clustering of the uni

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記成螞蟻

EnvSOM: A SOM Algorithm Conditioned on the Environment for Clustering and Visualizationts of two consecutive trainings of the self-organizing map. In the first phase, a SOM is trained using every available variable, but only those which characterize the environment are used to compute the winner unit. Therefore, this phase produces an accurate model of the environment. In the second p

fidelity

Cardioversion

Sparse Functional Relevance Learning in Generalized Learning Vector Quantization set of basis functions depending on only a few parameters compared to standard relevance learning. Moreover, the sparsity of the superposition is achieved by an entropy based penalty function forcing sparsity.

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diskitis

Requirements for the Learning of Multiple Dynamicsing Algorithm of Multiple Dynamics (LAMD), is expected to satisfy the following four requirements. (i) Given a set of time-series sequences for training, estimate the dynamics and their latent variables. (ii) Order the dynamical systems according to the similarities between them. (iii) Interpolate i