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Natural Computing For Unsupervised Learning


Natural Computing For Unsupervised Learning
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Natural Computing For Unsupervised Learning


Natural Computing For Unsupervised Learning
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Author : Xiangtao Li
language : en
Publisher: Springer
Release Date : 2018-10-31

Natural Computing For Unsupervised Learning written by Xiangtao Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Technology & Engineering categories.


This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms



Natural Computing Algorithms


Natural Computing Algorithms
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Author : Anthony Brabazon
language : en
Publisher: Springer
Release Date : 2015-10-08

Natural Computing Algorithms written by Anthony Brabazon and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-08 with Computers categories.


The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design. This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.



Reservoir Computing


Reservoir Computing
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Author : Kohei Nakajima
language : en
Publisher: Springer Nature
Release Date : 2021-08-05

Reservoir Computing written by Kohei Nakajima and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-05 with Computers categories.


This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.



Proceeding Of First Doctoral Symposium On Natural Computing Research


Proceeding Of First Doctoral Symposium On Natural Computing Research
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Author : Varsha H. Patil
language : en
Publisher: Springer Nature
Release Date : 2021-03-18

Proceeding Of First Doctoral Symposium On Natural Computing Research written by Varsha H. Patil and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-18 with Technology & Engineering categories.


The book is a collection of papers presented at First Doctoral Symposium on Natural Computing Research (DSNCR 2020), held during 8 August 2020 in Pune, India. The book covers different topics of applied and natural computing methods having applications in physical sciences and engineering. The book focuses on computer vision and applications, soft computing, security for Internet of Things, security in heterogeneous networks, signal processing, intelligent transportation system, VLSI design and embedded systems, privacy and confidentiality, big data and cloud computing, bioinformatics and systems biology, remote healthcare, software security, mobile and pervasive computing, biometrics-based authentication, natural language processing, analysis and verification techniques, large scale networking, distributed systems, digital forensics, and human–computer interaction.



Adaptive And Natural Computing Algorithms


Adaptive And Natural Computing Algorithms
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Author : Bernadete Ribeiro
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-12

Adaptive And Natural Computing Algorithms written by Bernadete Ribeiro 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 2005-12-12 with Computers categories.


The ICANNGA series of Conferences has been organised since 1993 and has a long history of promoting the principles and understanding of computational intelligence paradigms within the scientific community and is a reference for established workers in this area. Starting in Innsbruck, in Austria (1993), then to Ales in Prance (1995), Norwich in England (1997), Portoroz in Slovenia (1999), Prague in the Czech Republic (2001) and finally Roanne, in France (2003), the ICANNGA series has established itself for experienced workers in the field. The series has also been of value to young researchers wishing both to extend their knowledge and experience and also to meet internationally renowned experts. The 2005 Conference, the seventh in the ICANNGA series, will take place at the University of Coimbra in Portugal, drawing on the experience of previous events, and following the same general model, combining technical sessions, including plenary lectures by renowned scientists, with tutorials.



Fundamentals Of Natural Computing


Fundamentals Of Natural Computing
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Author : Leandro Nunes de Castro
language : en
Publisher: CRC Press
Release Date : 2006-06-02

Fundamentals Of Natural Computing written by Leandro Nunes de Castro and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-02 with Computers categories.


Natural computing brings together nature and computing to develop new computational tools for problem solving; to synthesize natural patterns and behaviors in computers; and to potentially design novel types of computers. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications presents a wide-ranging survey of novel techniqu



Handbook Of Research On Natural Computing For Optimization Problems


Handbook Of Research On Natural Computing For Optimization Problems
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Author : Mandal, Jyotsna Kumar
language : en
Publisher: IGI Global
Release Date : 2016-05-25

Handbook Of Research On Natural Computing For Optimization Problems written by Mandal, Jyotsna Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-25 with Computers categories.


Nature-inspired computation is an interdisciplinary topic area that connects the natural sciences to computer science. Since natural computing is utilized in a variety of disciplines, it is imperative to research its capabilities in solving optimization issues. The Handbook of Research on Natural Computing for Optimization Problems discusses nascent optimization procedures in nature-inspired computation and the innovative tools and techniques being utilized in the field. Highlighting empirical research and best practices concerning various optimization issues, this publication is a comprehensive reference for researchers, academicians, students, scientists, and technology developers interested in a multidisciplinary perspective on natural computational systems.



Nature Inspired Computing For Data Science


Nature Inspired Computing For Data Science
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Author : Minakhi Rout
language : en
Publisher: Springer Nature
Release Date : 2019-11-26

Nature Inspired Computing For Data Science written by Minakhi Rout and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-26 with Computers categories.


This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.



Artificial Intelligence Machine Learning And User Interface Design


Artificial Intelligence Machine Learning And User Interface Design
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Author : Abhijit Banubakode
language : en
Publisher: Bentham Science Publishers
Release Date : 2024-05-10

Artificial Intelligence Machine Learning And User Interface Design written by Abhijit Banubakode and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-10 with Computers categories.


Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies. The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains. Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management. Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.



Advanced Lectures On Machine Learning


Advanced Lectures On Machine Learning
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Author : Olivier Bousquet
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-09-02

Advanced Lectures On Machine Learning written by Olivier Bousquet 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 2004-09-02 with Computers categories.


Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.