Emerging Paradigms In Machine Learning

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Emerging Paradigms In Machine Learning

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Author : Sheela Ramanna
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-31

Emerging Paradigms In Machine Learning written by Sheela Ramanna 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 2012-07-31 with Technology & Engineering categories.

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.

Emerging Computing Paradigms

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Author : Umang Singh
language : en
Publisher: John Wiley & Sons
Release Date : 2022-07-12

Emerging Computing Paradigms written by Umang Singh 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 2022-07-12 with Computers categories.

EMERGING COMPUTING PARADIGMS A holistic overview of major new computing paradigms of the 21st Century In Emerging Computing Paradigms: Principles, Advances and Applications, international scholars offer a compendium of essential knowledge on new promising computing paradigms. The book examines the characteristics and features of emerging computing technologies and provides insight into recent technological developments and their potential real-world applications that promise to shape the future. This book is a useful resource for all those who wish to quickly grasp new concepts of, and insights on, emerging computer paradigms and pursue further research or innovate new novel applications harnessing these concepts. Key Features Presents a comprehensive coverage of new technologies that have the potential to shape the future of our world—quantum computing, computational intelligence, advanced wireless networks and blockchain technology Revisits mainstream ideas now being widely adopted, such as cloud computing, the Internet of Things (IoT) and cybersecurity Offers recommendations and practical insights to assist the readers in the application of these technologies Aimed at IT professionals, educators, researchers, and students, Emerging Computing Paradigms: Principles, Advances and Applications is a comprehensive resource to get ahead of the curve in examining and exploiting emerging new concepts and technologies. Business executives will also find the book valuable and gain an advantage over competitors in harnessing the concepts examined therein.

Artificial Intelligence

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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2021-09-01

Artificial Intelligence written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Computers categories.

Artificial Intelligence (AI) is widely known as a knowledge field that aims to make computers, robots, or products that mimic the way humans think. In the current scientific community, AI is an intensively studied area composed of multiple branches. Historically, machine learning and optimization are two of the most studied fronts thanks to the development of novel and challenging research topics such as transfer optimization, swarm robotics, and drift detection and adaptation to evolving conditions in real-time. This book collects radically new theoretical insights, reporting recent developments and evincing innovative applications regarding AI methods in all fields of knowledge. It also presents works focused on new paradigms and novel branches of AI science.

Emerging Paradigms In Digital Finance And Intelligent Economic Infrastructure

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Author :
language : en
Publisher: Global Pen Press UK
Release Date :

Emerging Paradigms In Digital Finance And Intelligent Economic Infrastructure written by and has been published by Global Pen Press UK this book supported file pdf, txt, epub, kindle and other format this book has been release on with Business & Economics categories.

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Emerging Paradigm Innovations And Insight In English Literature And Language Research In The Digital Age

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Author : DR. PRITEE JAIN
language : en
Publisher: RMSG PUBLICATION
Release Date : 2024-05-06

Emerging Paradigm Innovations And Insight In English Literature And Language Research In The Digital Age written by DR. PRITEE JAIN and has been published by RMSG PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-06 with Antiques & Collectibles categories.

This interdisciplinary book explores the intersection of literature, education, gender equality, and the digital revolution. We welcome original research, critical essays, and theoretical discussions that delve into the evolving dynamics shaping these areas.

Lifelong Machine Learning

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Author : Zhiyuan Chen
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2018-08-14

Lifelong Machine Learning written by Zhiyuan Chen and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-14 with Computers categories.

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Machine Learning Paradigms Theory And Application

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Author : Aboul Ella Hassanien
language : en
Publisher: Springer
Release Date : 2018-12-08

Machine Learning Paradigms Theory And Application written by Aboul Ella Hassanien and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-08 with Technology & Engineering categories.

The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.

Understanding Machine Learning

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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19

Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

New Learning Paradigms In Soft Computing

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Author : Lakhmi C. Jain
language : en
Publisher: Physica
Release Date : 2013-06-05

New Learning Paradigms In Soft Computing written by Lakhmi C. Jain 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.

Learning is a key issue in the analysis and design of all kinds of intelligent systems. In recent time many new paradigms of automated (machine) learning have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of science and technology, seems to offer new qualities in the realm of machine learning too. The purpose of this volume is to present some new learning paradigms that have been triggered, or at least strongly influenced by soft computing tools and techniques, mainly related to neural networks, fuzzy logic, rough sets, and evolutionary computations.

Efficient Learning Machines

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Author : Mariette Awad
language : en
Publisher: Apress
Release Date : 2015-04-27

Efficient Learning Machines written by Mariette Awad and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-27 with Computers categories.

Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.