Titlebook: Hybrid Value Creation; Vivek K. Velamuri Book 2013 Springer Fachmedien Wiesbaden 2013

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書目名稱Hybrid Value Creation影響因子(影響力)




書目名稱Hybrid Value Creation影響因子(影響力)學(xué)科排名




書目名稱Hybrid Value Creation網(wǎng)絡(luò)公開度




書目名稱Hybrid Value Creation網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Hybrid Value Creation被引頻次




書目名稱Hybrid Value Creation被引頻次學(xué)科排名




書目名稱Hybrid Value Creation年度引用




書目名稱Hybrid Value Creation年度引用學(xué)科排名




書目名稱Hybrid Value Creation讀者反饋




書目名稱Hybrid Value Creation讀者反饋學(xué)科排名

Ballerina

al data modeling and to move a step forward towards the understa- ing of the nervous system? Relevant here are the general philosophy of the IWANN conferences, the sustained interdisciplinary approach, and the global strategy, again and again to bring together physiologists and computer experts to c

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大猩猩

Decrepit

MEAN

藕床生厭倦

增長(zhǎng)

th the maximum dissimilarity to the already chosen subset, aiming to maximize the dissimilarity between samples within the subset and comprehensively cover the class distribution. Additionally, the model tends to favor new classes in decision-making due to the imbalance in class quantities. This pap

鐵塔等

Vivek K. Velamurith the maximum dissimilarity to the already chosen subset, aiming to maximize the dissimilarity between samples within the subset and comprehensively cover the class distribution. Additionally, the model tends to favor new classes in decision-making due to the imbalance in class quantities. This pap

獸皮

Vivek K. Velamuriltivariate Anomaly Detection framework with multi-scale Self-learned Graph Structures (MAD-SGS) based on the Variational Autoencoder (VAE) architecture. Specifically, the Long Short-Term Memory (LSTM) was applied to extract and exploit the temporal information, and the Graph Convolution Network (GCN