Описание: Constitutes the proceedings of the 13th conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, held in Dortmund, Germany, in June 2010.
Описание: The International Conference on Information Processing and Management of - certainty in Knowledge-Based Systems, IPMU, is organized every two years with the aim of bringing together scientists working on methods for the management of uncertainty and aggregation of information in intelligent systems.
Автор: Bernadette Bouchon-Meunier; Llorenc Valverde; Rona Название: IPMU`92 - Advanced Methods in Artificial Intelligence ISBN: 3540567356 ISBN-13(EAN): 9783540567356 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The papers in this volume are based on presentations at the fourth IPMU Conference, held in Palma de Mallorca, July 6-10, 1992. The conference focused on issues related to the acquisition, representation, management and transmission of information in knowledge-based and decision-making systems.
Описание: This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cadiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions.
Описание: This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cadiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions.
Описание: This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cadiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions.
Описание: This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. text analysis and processing; statistical image processing and analysis, with applications in neuroimaging;
Описание: This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. text analysis and processing; statistical image processing and analysis, with applications in neuroimaging;
Описание: This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. text analysis and processing; statistical image processing and analysis, with applications in neuroimaging;
Описание: This two-volume set (CCIS 1601-1602) constitutes the proceedings of the 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2021, held in Milan, Italy, in July 2022. mathematical fuzzy logics; fuzzy methods in data mining and knowledge discovery;
Описание: The book offers a comprehensive and timely overview of advanced mathematical tools for both uncertainty analysis and modeling of parallel processes, with a special emphasis on intuitionistic fuzzy sets and generalized nets.
Автор: Zhengming Ding; Handong Zhao; Yun Fu Название: Learning Representation for Multi-View Data Analysis ISBN: 3030007332 ISBN-13(EAN): 9783030007331 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal.A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
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