Granula Computing Via Covering Models
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Granula Computing Via Covering Models
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Author : Roshdey Mareay
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2013
Granula Computing Via Covering Models written by Roshdey Mareay and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
In this book, we try to find a connection between rough sets, fuzzy sets and Topology.Although there are differences among these theories of granular computing in their formulation, i.e., construction of granules and representation of their relationships, their core idea is the same. That is, a problem space is firstly divided into some basic granules. Then, these basic granules are further composed or decomposed into new granules at Different hierarchies. The above two steps are repeated until these new granules could solve the problem more valid.
Granular Computing Based Machine Learning
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Author : Han Liu
language : en
Publisher: Springer
Release Date : 2017-11-04
Granular Computing Based Machine Learning written by Han Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-04 with Technology & Engineering categories.
This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.
Performance Modeling For Computer Architects
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Author : C. M. Krishna
language : en
Publisher: John Wiley & Sons
Release Date : 1995-10-14
Performance Modeling For Computer Architects written by C. M. Krishna and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-10-14 with Computers categories.
As computers become more complex, the number and complexity of the tasks facing the computer architect have increased. Computer performance often depends in complex way on the design parameters and intuition that must be supplemented by performance studies to enhance design productivity. This book introduces computer architects to computer system performance models and shows how they are relatively simple, inexpensive to implement, and sufficiently accurate for most purposes. It discusses the development of performance models based on queuing theory and probability. The text also shows how they are used to provide quick approximate calculations to indicate basic performance tradeoffs and narrow the range of parameters to consider when determining system configurations. It illustrates how performance models can demonstrate how a memory system is to be configured, what the cache structure should be, and what incremental changes in cache size can have on the miss rate. A particularly deep knowledge of probability theory or any other mathematical field to understand the papers in this volume is not required.
Data Mining Rough Sets And Granular Computing
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Author : Tsau Young Lin
language : en
Publisher: Physica
Release Date : 2013-11-11
Data Mining Rough Sets And Granular Computing written by Tsau Young Lin and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Computers categories.
During the past few years, data mining has grown rapidly in visibility and importance within information processing and decision analysis. This is par ticularly true in the realm of e-commerce, where data mining is moving from a "nice-to-have" to a "must-have" status. In a different though related context, a new computing methodology called granular computing is emerging as a powerful tool for the conception, analysis and design of information/intelligent systems. In essence, data mining deals with summarization of information which is resident in large data sets, while granular computing plays a key role in the summarization process by draw ing together points (objects) which are related through similarity, proximity or functionality. In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a branch of granular computing. However, its applications to data mining have predated that of granular computing.
Advanced Anomaly Detection Technologies And Applications In Energy Systems
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Author : Tinghui Ouyang
language : en
Publisher: Frontiers Media SA
Release Date : 2022-10-14
Advanced Anomaly Detection Technologies And Applications In Energy Systems written by Tinghui Ouyang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-14 with Technology & Engineering categories.
Granular Computing And Big Data Advancements
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Author : Zhang, Chao
language : en
Publisher: IGI Global
Release Date : 2024-08-06
Granular Computing And Big Data Advancements written by Zhang, Chao and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-06 with Computers categories.
In an era defined by the deluge of data, navigating the complexities of decision-making under conditions of uncertainty has emerged as a formidable challenge for scholars and practitioners alike. The sheer volume and velocity of information inundating decision-makers often leads to paralysis or misguided choices, amplifying the risks inherent in uncertain environments. Granular Computing and Big Data Advancements provides insights and solutions in this challenging landscape. The impact of Granular Computing and Big Data Advancements reverberates across the research community, offering a cohesive resource that bridges the gap between theory and practice. With its interdisciplinary approach and emphasis on innovation, the book fosters collaboration and empowers scholars to tackle complex challenges head-on. Whether researchers seek novel methodologies, practitioners aim to enhance decision-making processes, or students embark on their academic journey, this publication serves as a cornerstone in the quest for effective decision-making amidst the uncertainties of the modern world.
Bio Inspired Models Of Network Information And Computing Systems
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Author : Emma Hart
language : en
Publisher: Springer
Release Date : 2012-08-10
Bio Inspired Models Of Network Information And Computing Systems written by Emma Hart and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-10 with Computers categories.
This book constitutes the thoroughly refereed post-conference proceedings of the 6th International Conference on Bio-Inspired Models of Network, Information, and Computing Systems (Bionetics). The event took place in the city of York, UK, in December 2011. Bionetics main objective is to bring bio-inspired paradigms into computer engineering and networking, and to enhance the fruitful interactions between these fields and biology. The papers of the conference were accepted in 2 categories: full papers and work-in progress. Full papers describe significant advances in the Bionetics field, while work-in-progress papers present an opportunity to discuss breaking research which is currently being evaluated. The topics are ranging from robotic coordination to attack detection in peer-to-peer networks, biological mechanisms including evolution, flocking and artificial immune systems, and nano-scale communication and networking.
Granular Fuzzy And Soft Computing
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Author : Tsau-Young Lin
language : en
Publisher: Springer Nature
Release Date : 2023-03-29
Granular Fuzzy And Soft Computing written by Tsau-Young Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-29 with Mathematics categories.
The first edition of the Encyclopedia of Complexity and Systems Science (ECSS, 2009) presented a comprehensive overview of granular computing (GrC) broadly divided into several categories: Granular computing from rough set theory, Granular Computing in Database Theory, Granular Computing in Social Networks, Granular Computing and Fuzzy Set Theory, Grid/Cloud Computing, as well as general issues in granular computing. In 2011, the formal theory of GrC was established, providing an adequate infrastructure to support revolutionary new approaches to computer/data science, including the challenges presented by so-called big data. For this volume of ECSS, Second Edition, many entries have been updated to capture these new developments, together with new chapters on such topics as data clustering, outliers in data mining, qualitative fuzzy sets, and information flow analysis for security applications. Granulations can be seen as a natural and ancient methodology deeply rooted in the human mind. Many daily "things" are routinely granulated into sub "things": The topography of earth is granulated into hills, plateaus, etc., space and time are granulated into infinitesimal granules, and a circle is granulated into polygons of infinitesimal sides. Such granules led to the invention of calculus, topology and non-standard analysis. Formalization of general granulation was difficult but, as shown in this volume, great progress has been made in combing discrete and continuous mathematics under one roof for a broad range of applications in data science.
Granular Computing
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Author : Witold Pedrycz
language : en
Publisher: Physica
Release Date : 2013-06-05
Granular Computing written by Witold Pedrycz and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-05 with Computers categories.
Granular Computing is concerned with constructing and processing carried out at the level of information granules. Using information granules, we comprehend the world and interact with it, no matter which intelligent endeavor this may involve. The landscape of granular computing is immensely rich and involves set theory (interval mathematics), fuzzy sets, rough sets, random sets linked together in a highly synergetic environment. This volume is a first comprehensive treatment of this emerging paradigm and embraces its fundamentals, underlying methodological framework, and a sound algorithmic environment. The panoply of applications covered includes system identification, telecommunications, linguistics and music processing. Written by experts in the field, this volume will appeal to all developing intelligent systems, either working at the methodological level or interested in detailed system realization.
Soft Computing For Knowledge Discovery
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Author : James G. Shanahan
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Soft Computing For Knowledge Discovery written by James G. Shanahan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Knowledge discovery is an area of computer science that attempts to uncover interesting and useful patterns in data that permit a computer to perform a task autonomously or assist a human in performing a task more efficiently. Soft Computing for Knowledge Discovery provides a self-contained and systematic exposition of the key theory and algorithms that form the core of knowledge discovery from a soft computing perspective. It focuses on knowledge representation, machine learning, and the key methodologies that make up the fabric of soft computing - fuzzy set theory, fuzzy logic, evolutionary computing, and various theories of probability (e.g. naïve Bayes and Bayesian networks, Dempster-Shafer theory, mass assignment theory, and others). In addition to describing many state-of-the-art soft computing approaches to knowledge discovery, the author introduces Cartesian granule features and their corresponding learning algorithms as an intuitive approach to knowledge discovery. This new approach embraces the synergistic spirit of soft computing and exploits uncertainty in order to achieve tractability, transparency and generalization. Parallels are drawn between this approach and other well known approaches (such as naive Bayes and decision trees) leading to equivalences under certain conditions. The approaches presented are further illustrated in a battery of both artificial and real-world problems. Knowledge discovery in real-world problems, such as object recognition in outdoor scenes, medical diagnosis and control, is described in detail. These case studies provide further examples of how to apply the presented concepts and algorithms to practical problems. The author provides web page access to an online bibliography, datasets, source codes for several algorithms described in the book, and other information. Soft Computing for Knowledge Discovery is for advanced undergraduates, professionals and researchers in computer science, engineering and business information systems who work or have an interest in the dynamic fields of knowledge discovery and soft computing.