Multidimensional Mining of Massive Text Data, Chao Zhang, Jiawei Han
Автор: Leskovec Jure Название: Mining of Massive Datasets ISBN: 1108476341 ISBN-13(EAN): 9781108476348 Издательство: Cambridge Academ Рейтинг: Цена: 71810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
Автор: Friedemann K?ster Название: Multidimensional Analysis of Conversational Telephone Speech ISBN: 9811353468 ISBN-13(EAN): 9789811353468 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Поставка под заказ. Описание: This book presents a new diagnostic information methodology to assess the quality of conversational telephone speech. For this, a conversation is separated into three individual conversational phases (listening, speaking, and interaction), and for each phase corresponding perceptual dimensions are identified. A new analytic test method allows gathering dimension ratings from non-expert test subjects in a direct way. The identification of the perceptual dimensions and the new test method are validated in two sophisticated conversational experiments. The dimension scores gathered with the new test method are used to determine the quality of each conversational phase, and the qualities of the three phases, in turn, are combined for overall conversational quality modeling. The conducted fundamental research forms the basis for the development of a preliminary new instrumental diagnostic conversational quality model. This multidimensional analysis of conversational telephone speech is a major landmark towards deeply analyzing conversational speech quality for diagnosis and optimization of telecommunication systems.
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Автор: Jacob Kogan; Charles Nicholas; Marc Teboulle Название: Grouping Multidimensional Data ISBN: 3642066542 ISBN-13(EAN): 9783642066542 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.Kogan and his co-editors have put together recent advances in clustering large and high-dimension data.
Автор: Friedemann K?ster Название: Multidimensional Analysis of Conversational Telephone Speech ISBN: 9811052239 ISBN-13(EAN): 9789811052231 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents a new diagnostic information methodology to assess the quality of conversational telephone speech. The dimension scores gathered with the new test method are used to determine the quality of each conversational phase, and the qualities of the three phases, in turn, are combined for overall conversational quality modeling.
Автор: Donghui Lin; Toru Ishida; Franco Zambonelli; Itsuk Название: Massively Multi-Agent Systems II ISBN: 3030209369 ISBN-13(EAN): 9783030209360 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book contains revised selected and invited papers presented at the International Workshop on Massively Multi-Agent Systems, MMAS 2018, held in Stockholm, Sweden, in July 2018.
The 7 revised full papers presented were carefully reviewed and selected for inclusion in this volume. Also included are 3 post-workshop papers. The papers discuss enabling technologies, new architectures, promising applications, and challenges of massively multi-agent systems in the era of IoT. They are organized in the following topical sections: multi-agent systems and Internet of Things; architectures for massively multi-agent systems; and applications of massively multi-agent systems.
Автор: Zhang Chao, Han Jiawei Название: Multidimensional Mining of Massive Text Data ISBN: 1681735199 ISBN-13(EAN): 9781681735191 Издательство: Mare Nostrum (Eurospan) Цена: 77610.00 T Наличие на складе: Невозможна поставка. Описание:
Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional-they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.
This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.
The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.
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