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

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发表于 2025-3-21 18:35:32 | 显示全部楼层 |阅读模式
书目名称Hybrid Value Creation
编辑Vivek K. Velamuri
视频video
概述Systematic Assessment of the State-of-the-art.Includes supplementary material:
丛书名称Markt- und Unternehmensentwicklung Markets and Organisations
图书封面Titlebook: Hybrid Value Creation;  Vivek K. Velamuri Book 2013 Springer Fachmedien Wiesbaden 2013
描述This work deals with hybrid value creation, i.e., the process of generating additional value by innovatively combining products (tangible component) and services (intangible component). Vivek K. Velamuri provides a systematic assessment of the state-of-the-art of the field and identifies empirically derived strategies for hybrid value creation. In addition, it helps practitioners to come to grips with understanding the dynamics of hybrid value creation, irrespective of the industry they are in. At the same time directions for future research are identified and provided.​
出版日期Book 2013
版次1
doihttps://doi.org/10.1007/978-3-8349-3961-6
isbn_softcover978-3-8349-3960-9
isbn_ebook978-3-8349-3961-6Series ISSN 2945-879X Series E-ISSN 2945-8803
issn_series 2945-879X
copyrightSpringer Fachmedien Wiesbaden 2013
The information of publication is updating

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发表于 2025-3-21 20:18:36 | 显示全部楼层
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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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
发表于 2025-3-23 02:58:34 | 显示全部楼层
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
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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
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