Автор: Lavrač Nada, Podpečan VID, Robnik-Sikonja Marko Название: Representation Learning: Propositionalization and Embeddings ISBN: 303068816X ISBN-13(EAN): 9783030688165 Издательство: Springer Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This monograph addresses advances in representation learning, a cutting-edge research area of machine learning.
Автор: Yang, Cheng Liu, Zhiyuan Tu, Cunchao Shi, Chuan Sun, Maosong Название: Network Embedding ISBN: 3031004620 ISBN-13(EAN): 9783031004629 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Muandet, Krikamol Fukumizu, Kenji Sriperumbudur, Bharath Scholkopf, Bernhard Название: Kernel mean embedding of distributions: ISBN: 1680832883 ISBN-13(EAN): 9781680832884 Издательство: Неизвестно Рейтинг: Цена: 13656.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Jiang Chunxiao, Zhang Peiying Название: Qos-Aware Virtual Network Embedding ISBN: 9811652201 ISBN-13(EAN): 9789811652202 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Yun Fu; Yunqian Ma Название: Graph Embedding for Pattern Analysis ISBN: 1489990623 ISBN-13(EAN): 9781489990624 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao Название: Network Embedding: Theories, Methods, and Applications ISBN: 1636390447 ISBN-13(EAN): 9781636390444 Издательство: Mare Nostrum (Eurospan) Рейтинг: 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.
Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao Название: Network Embedding: Theories, Methods, and Applications ISBN: 1636390463 ISBN-13(EAN): 9781636390468 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 15939.00 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.
Автор: Lavra? Название: Representation Learning ISBN: 3030688194 ISBN-13(EAN): 9783030688196 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This monograph addresses advances in representation learning, a cutting-edge research area of machine learning.
Автор: Pilehvar, Mohammad Taher Camacho-collados, Jose Название: Embeddings in natural language processing ISBN: 1636390218 ISBN-13(EAN): 9781636390215 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 9286.00 р. Наличие на складе: Нет в наличии.
Описание: Provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings.
Автор: Pilehvar, Mohammad Taher Camacho-Collados, Jose Название: Embeddings in Natural Language Processing ISBN: 3031010493 ISBN-13(EAN): 9783031010491 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings.
Автор: Pilehvar, Mohammad Taher Camacho-collados, Jose Название: Embeddings in natural language processing ISBN: 1636390234 ISBN-13(EAN): 9781636390239 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12058.00 р. Наличие на складе: Нет в наличии.
Описание: Provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings.
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