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.
Machine Learning Paradigms
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Author : Maria Virvou
language : en
Publisher: Springer
Release Date : 2019-03-16
Machine Learning Paradigms written by Maria Virvou and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-16 with Technology & Engineering categories.
This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.
Machine Learning Paradigms
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Author : George A. Tsihrintzis
language : en
Publisher: Springer
Release Date : 2018-07-03
Machine Learning Paradigms written by George A. Tsihrintzis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-03 with Technology & Engineering categories.
This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.
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.
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.
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.
Machine Learning Paradigms
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Author : George A. Tsihrintzis
language : en
Publisher: Springer
Release Date : 2019-07-06
Machine Learning Paradigms written by George A. Tsihrintzis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-06 with Technology & Engineering categories.
This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
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.
Multi Disciplinary Approach To Research Emerging Paradigms
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Author : Dr. Santosh Dhar
language : en
Publisher: Allied Publishers
Release Date : 2022-03-26
Multi Disciplinary Approach To Research Emerging Paradigms written by Dr. Santosh Dhar and has been published by Allied Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-26 with Business & Economics categories.
Multidisciplinary approach in research is very much in vogue these days to address the problems of the society. It involves drawing appropriately from multiple disciplines to explore problems outside the normal boundaries and reach out to solutions addressed through different perspectives. Modern research looks through more multidisciplinary approaches and has dominance of problem solving and project oriented applied research. Multidisciplinary approaches while aiming at achieving a common goal attempts to develop answers to complex questions, which a single discipline is unable to handle. The growing research canon is to apply knowledge of various disciplines for the solution. Since current problems are of complex nature, there is a need to have knowledge of all the aspects such as economic, social, political and psychological. Multi-disciplinary approaches call for collaboration between two or more disciplines on a research project, while each discipline maintaining its assumptions, values, and methods. In other words, each discipline maintains its autonomy while collaborating. Today multidisciplinary approach is considered as the driver of innovation and research to solve real world problems. The book aims to address the current issues and problems and draw the solutions with the help of multidisciplinary approaches. Key Features · Highlights the aspects of experiential marketing in higher education institutions, social and emotional learning for children, customer relationship and purchase intention of customers on digital platform, theoretical contribution and evaluation of HRA, Normative susceptibility towards counterfeit branded products, workplace spirituality in enhancing employee well-being and artworks revolved around the religious deities and kings. · Describes innovative solutions towards excess runoff, continuous monitoring of train parameters, recovering the infected individuals and reduction of their number, compete for achieving the growth and respectable market share, security and privacy issues with the Smart Contract and improve the security of the blockchain technology. · Throws light on the techniques and their applications for Emperor Penguin Optimizer as a new power allocation approach, Latent finger-marks, QCA technology, better retrieval of invisible texts. · Focuses on gold has a strong hedge, economic impact of Mughals on Assamese society, Indian exports for improving productivity, loan repayment behaviours of the borrowers, positive attitude towards Swayam Courses. Academicians, researchers, practitioners, and students would be benefitted by reading this book.
Machine Learning Paradigms
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Author : Aristomenis S. Lampropoulos
language : en
Publisher: Springer
Release Date : 2015-06-13
Machine Learning Paradigms written by Aristomenis S. Lampropoulos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-13 with Technology & Engineering categories.
This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.