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Qos-Aware Virtual Network Embedding, Jiang Chunxiao, Zhang Peiying


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При оформлении заказа до: 2025-07-28
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Автор: Jiang Chunxiao, Zhang Peiying
Название:  Qos-Aware Virtual Network Embedding
ISBN: 9789811652202
Издательство: Springer
Классификация:


ISBN-10: 9811652201
Обложка/Формат: Hardcover
Страницы: 412
Вес: 0.75 кг.
Дата издания: 24.10.2021
Язык: English
Издание: 1st ed. 2021
Иллюстрации: 97 illustrations, color; 45 illustrations, black and white; x, 401 p. 142 illus., 97 illus. in color.
Размер: 23.39 x 15.60 x 2.39 cm
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Therefore, network resources need to be reasonably allocated according to users` QoS requirements to avoid the waste of network resources.In this book, based on the analysis of the principle of VNE algorithm, we provide a VNE scheme for users with differentiated QoS requirements.


Transfer learning through embedding spaces

Автор: Rostami, Mohammad
Название: Transfer learning through embedding spaces
ISBN: 0367699052 ISBN-13(EAN): 9780367699055
Издательство: Taylor&Francis
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Цена: 17609.00 р.
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Описание: Transfer Learning through Embedding Spaces provides a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities.

Network Embedding: Theories, Methods, and Applications

Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao
Название: Network Embedding: Theories, Methods, and Applications
ISBN: 1636390463 ISBN-13(EAN): 9781636390468
Издательство: Mare Nostrum (Eurospan)
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Цена: 15939.00 р.
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Описание:

Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.

This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.

Network Embedding: Theories, Methods, and Applications

Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao
Название: Network Embedding: Theories, Methods, and Applications
ISBN: 1636390447 ISBN-13(EAN): 9781636390444
Издательство: Mare Nostrum (Eurospan)
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Цена: 12751.00 р.
Наличие на складе: Нет в наличии.

Описание:

Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.

This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.

Satellite Network Robust QoS-aware Routing

Автор: Fei Long
Название: Satellite Network Robust QoS-aware Routing
ISBN: 3642543529 ISBN-13(EAN): 9783642543524
Издательство: Springer
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Цена: 16979.00 р.
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Описание: Satellite Network Robust QoS-aware Routing presents a novel routing strategy for satellite networks.

Graph Embedding for Pattern Analysis

Автор: Yun Fu; Yunqian Ma
Название: Graph Embedding for Pattern Analysis
ISBN: 1489990623 ISBN-13(EAN): 9781489990624
Издательство: Springer
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Цена: 16977.00 р.
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Описание: This book presents advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph and graph in vector spaces, and describes their real-world applications.

Kernel mean embedding of distributions:

Автор: Muandet, Krikamol Fukumizu, Kenji Sriperumbudur, Bharath Scholkopf, Bernhard
Название: Kernel mean embedding of distributions:
ISBN: 1680832883 ISBN-13(EAN): 9781680832884
Издательство: Неизвестно
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Цена: 13656.00 р.
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Описание: This monograph provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics who are interested in the theory and applications of kernel mean embeddings.

Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs

Автор: Aggarwal Manasvi, Murty M. N.
Название: Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs
ISBN: 9813340215 ISBN-13(EAN): 9789813340213
Издательство: Springer
Цена: 9083.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание:

Introduction

1.1 introduction

1.2 Notations used in Book

1.3 Contents covered in this book

2 Representations of Networks

2.1 Introduction

2.2 Networks Represented as Graphs

2.3 Data Structures to Represent Graphs

2.3.1 Matrix Representation

2.3.2 Adjacency List

2.4 Network Embeddings

2.5 Evaluation Datasets

2.5.1 Evaluation Datasets

2.5.2 Evaluation Metrics

2.6 Machine Learning Downstream Tasks

2.6.1 Classification

2.6.2 Clustering

2.6.3 Link Prediction (LP)

2.6.4 Visualization

2.6.5 Network Reconstruction

2.7 Embeddings based on Matrix Factorization

2.7.1 Singular Value Decomposition (SVD)

2.7.2 Matrix Factorization based Clustering

2.7.3 Soft Clustering as Matrix Factorization

2.7.4 Non-negative Matrix factorization (NMF)

2.8 Word2vec

2.8.1 Skipgram model

2.9 Learning Network Embeddings

2.9.1 Supervised Learning

2.9.2 Unsupervised Learning

2.9.3 Node and Edge Embeddings

2.9.4 Graph Embedding

2.10 Summary

3 Deep Learning

3.1 Introduction

3.2 Neural Networks

3.2.1 Perceptron

3.2.2 Characteristics of Neural Networks

3.2.3 Multilayer Perceptron Networks

3.2.4 Training MLP Networks

3.3 Convolution Neural Networks

3.3.1 Activation Function

3.3.2 Initialization of Weights

3.3.3 Deep Feedforward Neural Network

3.4 Recurrent Networks

3.4.1 Recurrent Neural Networks

3.4.2 Long Short Term Memory

3.4.3 Different Gates used by LSTM

3.4.4 Training of LSTM Models

3.5 Learning Representations using Autoencoders

3.5.1 Types of Autoencoders

3.6 Summary

References

4 Embedding Nodes and Edge

4.1 Introduction

4.2 Representation of Node and Edges as Vectors

4.3 Embeddings based on Random Walks

4.4 Embeddings based on Matrix Factorization

4.5 Graph Neural Network Models

4.6 State of the art algorithms

4.7 Evaluation methods and Machine Learning tasks

4.8 Summary

References

5 Embedding Graphs

5.1 Introduction

5.2 Representation of Graphs as Vectors

5.3 Graph Representation using Node Embeddings

5.4 Graph Pooling Techniques

5.4.1 Global Pooling Methods

5.4.2 Hierarchical Pooling Methods

5.5 State of the art algorithms

5.6 Evaluation methods and Machine Learning tasks

5.7 Summary

References
Context-Aware Systems and Applications: 10th EAI International Conference, ICCASA 2021, Virtual Event, October 28-29, 2021, Proceedings

Автор: Cong Vinh Phan, Rakib Abdur
Название: Context-Aware Systems and Applications: 10th EAI International Conference, ICCASA 2021, Virtual Event, October 28-29, 2021, Proceedings
ISBN: 3030931781 ISBN-13(EAN): 9783030931780
Издательство: Springer
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Цена: 11179.00 р.
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Описание: This book constitutes the refereed post-conference proceedings of the International Conference on Context-Aware Systems and Applications, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 25 revised full papers presented were carefully selected from 52 submissions.

Network-Aware Security for Group Communications

Автор: Yan Sun; Wade Trappe; K. J. Ray Liu
Название: Network-Aware Security for Group Communications
ISBN: 1441943358 ISBN-13(EAN): 9781441943354
Издательство: Springer
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Цена: 16977.00 р.
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Описание: It focuses on tailoring the security solution to the underlying network architecture (such as the wireless cellular network or the ad hoc/sensor network), or to the application using the security methods (such as multimedia multicasts).

OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis

Автор: Danail Stoyanov; Zeike Taylor; Duygu Sarikaya; Jon
Название: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis
ISBN: 303001200X ISBN-13(EAN): 9783030012007
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book constitutes the refereed joint proceedings of the First International Workshop on OR 2.0 Context-Aware Operating Theaters, OR 2.0 2018, 5th International Workshop on Computer Assisted Robotic Endoscopy, CARE 2018, 7th International Workshop on Clinical Image-Based Procedures, CLIP 2018, and the First International Workshop on Skin Image Analysis, ISIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 11 full papers presented at OR 2.0 2018, the 5 full papers presented at CARE 2018, the 8 full papers presented at CLIP 2018, and the 10 full papers presented at ISIC 2018 were carefully reviewed and selected.The OR 2.0 papers cover a wide range of topics such as machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors.The CARE papers cover topics to advance the field of computer-assisted and robotic endoscopy.The CLIP papers cover topics to fill gaps between basic science and clinical applications. The ISIC papers cover topics to facilitate knowledge dissemination in the field of skin image analysis, as well as to host a melanoma detection challenge, raising awareness and interest for these socially valuable tasks.

Advances in Culturally-Aware Intelligent Systems and in Cross-Cultural Psychological Studies

Автор: Colette Faucher
Название: Advances in Culturally-Aware Intelligent Systems and in Cross-Cultural Psychological Studies
ISBN: 3319883658 ISBN-13(EAN): 9783319883656
Издательство: Springer
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Цена: 20962.00 р.
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Описание: This book offers valuable new insights into the design of culturally-aware systems. In its first part, it is devoted to presenting selected Culturally-Aware Intelligent Systems devised in the field of Artificial Intelligence and its second part consists of two sub-parts that offer a source of inspiration for building modelizations of Culture and of its influence on the human mind and behavior, to be used in new Culturally-Aware Intelligent Systems. Those sub-parts present the results of experiments conducted in two fields that study Culture and its influence on the human mind’s functions: Cultural Neuroscience and Cross-Cultural Psychology. In this era of globalization, people from different countries and cultures have the opportunity to interact directly or indirectly in a wide variety of contexts. Despite differences in their ways of thinking and reasoning, their behaviors, their values, lifestyles, customs and habits, languages, religions – in a word, their cultures – they must be able to collaborate on projects, to understand each other’s views, to communicate in such a way that they don’t offend each other, to anticipate the effects of their actions on others, and so on. As such, it is of primary importance to understand how culture affects people’s mental activities, such as perception, interpretation, reasoning, emotion and behavior, in order to anticipate possible misunderstandings due to differences in handling the same situation, and to try and resolve them. Artificial Intelligence, and more specifically, the field of Intelligent Systems design, aims at building systems that mimic the behavior of human beings in order to complete tasks more efficiently than humans could by themselves. Consequently, in the last decade, experts and scholars in the field of Intelligent Systems have been increasingly tackling the notion of cultural awareness. A Culturally-Aware Intelligent System can be defined as a system where Culture-related or, more generally, socio-cultural information is modeled and used to design the human-machine interface, or to provide support with the task carried out by the system, be it reasoning, simulation or any other task involving cultural knowledge.


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