Machine Learning Techniques For Multimedia

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Machine Learning Techniques For Multimedia


Machine Learning Techniques For Multimedia
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Machine Learning Techniques For Multimedia


Machine Learning Techniques For Multimedia
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Author : Matthieu Cord
language : en
Publisher: Springer
Release Date : 2014-09-23

Machine Learning Techniques For Multimedia written by Matthieu Cord and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-23 with Computers categories.


Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.

Machine Learning For Multimedia Content Analysis


Machine Learning For Multimedia Content Analysis
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Author : Yihong Gong
language : en
Publisher: Springer
Release Date : 2010-02-12

Machine Learning For Multimedia Content Analysis written by Yihong Gong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-12 with Computers categories.


This volume introduces machine learning techniques that are particularly powerful and effective for modeling multimedia data and common tasks of multimedia content analysis. It systematically covers key machine learning techniques in an intuitive fashion and demonstrates their applications through case studies. Coverage includes examples of unsupervised learning, generative models and discriminative models. In addition, the book examines Maximum Margin Markov (M3) networks, which strive to combine the advantages of both the graphical models and Support Vector Machines (SVM).

Machine Learning Techniques For Adaptive Multimedia Retrieval


Machine Learning Techniques For Adaptive Multimedia Retrieval
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Author : Chia-Hung Wei
language : en
Publisher: IGI Global Snippet
Release Date : 2011

Machine Learning Techniques For Adaptive Multimedia Retrieval written by Chia-Hung Wei and has been published by IGI Global Snippet this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.


Machine Learning Techniques for Adaptive Multimedia Retrieval: Technologies Applications and Perspectives disseminates current information on multimedia retrieval, advances the field of multimedia databases, and educates the multimedia database community. It is a critical text for professionals who are engaged in efforts to understand machine learning techniques for adaptive multimedia retrieval research, design and applications.

Machine Learning For Intelligent Multimedia Analytics


Machine Learning For Intelligent Multimedia Analytics
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Author : Pardeep Kumar
language : en
Publisher: Springer Nature
Release Date : 2021-01-16

Machine Learning For Intelligent Multimedia Analytics written by Pardeep Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-16 with Technology & Engineering categories.


This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these large-scale data and also presents innovative solutions to address several potential research problems, e.g., enabling comprehensive visual classification to fill the semantic gap by exploring large-scale data, offering a promising frontier for detailed multimedia understanding, as well as extract patterns and making effective decisions by analyzing the large collection of data.

Machine Learning Techniques For Adaptive Multimedia Retrieval Technologies Applications And Perspectives


Machine Learning Techniques For Adaptive Multimedia Retrieval Technologies Applications And Perspectives
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Author : Wei, Chia-Hung
language : en
Publisher: IGI Global
Release Date : 2010-10-31

Machine Learning Techniques For Adaptive Multimedia Retrieval Technologies Applications And Perspectives written by Wei, Chia-Hung and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-31 with Computers categories.


"This book disseminates current information on multimedia retrieval, advancing the field of multimedia databases, and educating the multimedia database community on machine learning techniques for adaptive multimedia retrieval research, design and applications"--Provided by publisher.

Deep Learning Techniques And Optimization Strategies In Big Data Analytics


Deep Learning Techniques And Optimization Strategies In Big Data Analytics
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Author : Thomas, J. Joshua
language : en
Publisher: IGI Global
Release Date : 2019-11-29

Deep Learning Techniques And Optimization Strategies In Big Data Analytics written by Thomas, J. Joshua and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Computers categories.


Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Machine And Deep Learning Algorithms And Applications


Machine And Deep Learning Algorithms And Applications
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Author : Uday Shankar
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Machine And Deep Learning Algorithms And Applications written by Uday Shankar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.


This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.