Encyclopedia Of Machine Learning
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Encyclopedia Of Machine Learning
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Author : Claude Sammut
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-28
Encyclopedia Of Machine Learning written by Claude Sammut 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 2011-03-28 with Computers categories.
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Encyclopedia Of Machine Learning And Data Mining
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Author : Claude Sammut
language : en
Publisher:
Release Date :
Encyclopedia Of Machine Learning And Data Mining written by Claude Sammut and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Machine learning categories.
Encyclopedia Of Machine Learning
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Author : Robert Spencer
language : en
Publisher:
Release Date : 2025-06-17
Encyclopedia Of Machine Learning written by Robert Spencer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Reference categories.
Encyclopedia Of Data Science And Machine Learning
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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2023-01-20
Encyclopedia Of Data Science And Machine Learning written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Computers categories.
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Encyclopedia Of The Sciences Of Learning
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Author : Norbert M. Seel
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-10-05
Encyclopedia Of The Sciences Of Learning written by Norbert M. Seel 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 2011-10-05 with Education categories.
Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.
Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23
Mathematics For Machine Learning written by Marc Peter Deisenroth 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 2020-04-23 with Computers categories.
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Encyclopedia Of Machine Learning
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Author : Claude Sammut
language : en
Publisher: Springer
Release Date : 2010-11-12
Encyclopedia Of Machine Learning written by Claude Sammut and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-12 with Computers categories.
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Artificial Intelligence
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Author : Joost Nico Kok
language : en
Publisher: EOLSS Publications
Release Date : 2009-12-20
Artificial Intelligence written by Joost Nico Kok and has been published by EOLSS Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12-20 with Artificial intelligence categories.
Artificial Intelligence is a component of Encyclopedia of Technology, Information, and Systems Management Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty Encyclopedias. The Theme on Artificial Intelligence provides the essential aspects and fundamentals of Artificial Intelligence: Definition, Trends, Techniques, and Cases; Logic in Artificial Intelligence (AI); Computational Intelligence; Knowledge Based System Development Tools. It is aimed at the following five major target audiences: University and College Students, Educators, Professional Practitioners, Research Personnel and Policy Analysts, Managers, and Decision Makers.
Handbook Of Research On Applications And Implementations Of Machine Learning Techniques
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Author : Sathiyamoorthi Velayutham
language : en
Publisher: IGI Global, Engineering Science Reference
Release Date : 2019-08-23
Handbook Of Research On Applications And Implementations Of Machine Learning Techniques written by Sathiyamoorthi Velayutham and has been published by IGI Global, Engineering Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Computers categories.
"This book examines the practical applications and implementation of various machine learning techniques in various fields such as agriculture, medical, image processing, and networking"--
Confluence Of Ai Machine And Deep Learning In Cyber Forensics
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Author : Misra, Sanjay
language : en
Publisher: IGI Global
Release Date : 2020-12-18
Confluence Of Ai Machine And Deep Learning In Cyber Forensics written by Misra, Sanjay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-18 with Law categories.
Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication.