Machine Learning Paradigms
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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.
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 : 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 Computers 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.
Algorithms In Machine Learning Paradigms
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Author : Jyotsna Kumar Mandal
language : en
Publisher: Springer Nature
Release Date : 2020-01-03
Algorithms In Machine Learning Paradigms written by Jyotsna Kumar Mandal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-03 with Technology & Engineering categories.
This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.
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.
Fusion Of Machine Learning Paradigms
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Author : Ioannis K. Hatzilygeroudis
language : en
Publisher: Springer Nature
Release Date : 2023-02-06
Fusion Of Machine Learning Paradigms written by Ioannis K. Hatzilygeroudis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-06 with Technology & Engineering categories.
This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.
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.
Machine Learning
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Author : Jaime Guillermo Carbonell
language : en
Publisher: Mit Press
Release Date : 1990
Machine Learning written by Jaime Guillermo Carbonell and has been published by Mit Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
Explains the major paradigms for machine learning: inductive approaches, explanation-based learning, genetic algorithms and connectionist learning methods.
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.
New Learning Paradigms In Soft Computing
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Author : Lakhmi C. Jain
language : en
Publisher: Springer Science & Business Media
Release Date : 2002
New Learning Paradigms In Soft Computing written by Lakhmi C. Jain 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 2002 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.