Network Embedding: Theories, Methods, and Applications, Yang Cheng, Liu Zhiyuan, Tu Cunchao
Автор: Sogaard Anders, Vulic Ivan, Ruder Sebastian Название: Cross-Lingual Word Embeddings ISBN: 1681730634 ISBN-13(EAN): 9781681730639 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 61910.00 T Наличие на складе: Невозможна поставка. Описание:
The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano-and most other languages-remains limited.
Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages.
In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic.
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: 85010.00 T 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: Rostami, Mohammad Название: Transfer learning through embedding spaces ISBN: 0367699052 ISBN-13(EAN): 9780367699055 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
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: 91040.00 T 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: Yun Fu; Yunqian Ma Название: Graph Embedding for Pattern Analysis ISBN: 1489990623 ISBN-13(EAN): 9781489990624 Издательство: Springer Рейтинг: Цена: 113180.00 T 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: Sogaard Anders, Vulic Ivan, Ruder Sebastian Название: Cross-Lingual Word Embeddings ISBN: 1681735725 ISBN-13(EAN): 9781681735726 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 82230.00 T Наличие на складе: Невозможна поставка. Описание:
The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano-and most other languages-remains limited.
Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages.
In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic.
Автор: Ragozini Giancarlo, Vitale Maria Prosperina Название: Challenges in Social Network Research: Methods and Applications ISBN: 3030314650 ISBN-13(EAN): 9783030314651 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Some authors explore new trends related to network measures, multilevel networks and clustering on networks, while other contributions deepen the relationship among statistical methods for data mining and social network analysis.
Автор: Maoguo Gong; Linqiang Pan; Tao Song; Gexiang Zhang Название: Bio-inspired Computing – Theories and Applications ISBN: 9811036101 ISBN-13(EAN): 9789811036101 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The two-volume set, CCIS 681 and CCIS 682, constitutes the proceedings of the 11th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2016, held in Xi`an, China, in October 2016.The 115 revised full papers presented were carefully reviewed and selected from 343 submissions.
Автор: Jianyong Qiao; Xinchao Zhao; Linqiang Pan; Xingqua Название: Bio-inspired Computing: Theories and Applications ISBN: 9811328285 ISBN-13(EAN): 9789811328282 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This two-volume set (CCIS 951 and CCIS 952) constitutes the proceedings of the 13th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2018, held in Beijing, China, in November 2018.The 88 full papers presented in both volumes were selected from 206 submissions. The papers deal with studies abstracting computing ideas such as data structures, operations with data, ways to control operations, computing models from living phenomena or biological systems such as evolution, cells, neural networks, immune systems, swarm intelligence.
Автор: Bin Shi; S. S. Iyengar Название: Mathematical Theories of Machine Learning - Theory and Applications ISBN: 3030170756 ISBN-13(EAN): 9783030170752 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.
Автор: Jianyong Qiao; Xinchao Zhao; Linqiang Pan; Xingqua Название: Bio-inspired Computing: Theories and Applications ISBN: 9811328250 ISBN-13(EAN): 9789811328251 Издательство: Springer Рейтинг: Цена: 85710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This two-volume set (CCIS 951 and CCIS 952) constitutes the proceedings of the 13th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2018, held in Beijing, China, in November 2018.The 88 full papers presented in both volumes were selected from 206 submissions. The papers deal with studies abstracting computing ideas such as data structures, operations with data, ways to control operations, computing models from living phenomena or biological systems such as evolution, cells, neural networks, immune systems, swarm intelligence.
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