Learning Representation For Multi View Data Analysis

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Learning Representation for Multi-View Data Analysis

Author : Zhengming Ding
Publisher : Springer
ISBN 13 : 3030007340
Total Pages : 268 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Learning Representation for Multi-View Data Analysis by : Zhengming Ding

Download or read book Learning Representation for Multi-View Data Analysis written by Zhengming Ding and published by Springer. This book was released on 2018-12-06 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Recent Applications in Data Clustering

Author : Harun Pirim
Publisher : BoD – Books on Demand
ISBN 13 : 178923526X
Total Pages : 250 pages
Book Rating : 4.7/5 (892 download)

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Book Synopsis Recent Applications in Data Clustering by : Harun Pirim

Download or read book Recent Applications in Data Clustering written by Harun Pirim and published by BoD – Books on Demand. This book was released on 2018-08-01 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustering has emerged as one of the more fertile fields within data analytics, widely adopted by companies, research institutions, and educational entities as a tool to describe similar/different groups. The book Recent Applications in Data Clustering aims to provide an outlook of recent contributions to the vast clustering literature that offers useful insights within the context of modern applications for professionals, academics, and students. The book spans the domains of clustering in image analysis, lexical analysis of texts, replacement of missing values in data, temporal clustering in smart cities, comparison of artificial neural network variations, graph theoretical approaches, spectral clustering, multiview clustering, and model-based clustering in an R package. Applications of image, text, face recognition, speech (synthetic and simulated), and smart city datasets are presented.

Robust Representation for Data Analytics

Author : Sheng Li
Publisher : Springer
ISBN 13 : 3319601768
Total Pages : 224 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Robust Representation for Data Analytics by : Sheng Li

Download or read book Robust Representation for Data Analytics written by Sheng Li and published by Springer. This book was released on 2017-08-09 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Recent Advancements in Multi-View Data Analytics

Author : Witold Pedrycz
Publisher : Springer Nature
ISBN 13 : 3030952398
Total Pages : 346 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Recent Advancements in Multi-View Data Analytics by : Witold Pedrycz

Download or read book Recent Advancements in Multi-View Data Analytics written by Witold Pedrycz and published by Springer Nature. This book was released on 2022-05-20 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others. The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.

Multi-aspect Learning

Author : Richi Nayak
Publisher : Springer Nature
ISBN 13 : 3031335600
Total Pages : 191 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Multi-aspect Learning by : Richi Nayak

Download or read book Multi-aspect Learning written by Richi Nayak and published by Springer Nature. This book was released on 2023-08-28 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.

Multiview Machine Learning

Author : Shiliang Sun
Publisher : Springer
ISBN 13 : 9811330298
Total Pages : 149 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Multiview Machine Learning by : Shiliang Sun

Download or read book Multiview Machine Learning written by Shiliang Sun and published by Springer. This book was released on 2019-01-07 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.

Prediction and Analysis for Knowledge Representation and Machine Learning

Author : Avadhesh Kumar
Publisher : CRC Press
ISBN 13 : 100048422X
Total Pages : 216 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Prediction and Analysis for Knowledge Representation and Machine Learning by : Avadhesh Kumar

Download or read book Prediction and Analysis for Knowledge Representation and Machine Learning written by Avadhesh Kumar and published by CRC Press. This book was released on 2022-01-31 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which includes both basic and advanced concepts.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

AI 2023: Advances in Artificial Intelligence

Author : Tongliang Liu
Publisher : Springer Nature
ISBN 13 : 9819983886
Total Pages : 574 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis AI 2023: Advances in Artificial Intelligence by : Tongliang Liu

Download or read book AI 2023: Advances in Artificial Intelligence written by Tongliang Liu and published by Springer Nature. This book was released on 2023-11-26 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm.

Behavior Analysis with Machine Learning Using R

Author : Enrique Garcia Ceja
Publisher : CRC Press
ISBN 13 : 1000484238
Total Pages : 434 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Behavior Analysis with Machine Learning Using R by : Enrique Garcia Ceja

Download or read book Behavior Analysis with Machine Learning Using R written by Enrique Garcia Ceja and published by CRC Press. This book was released on 2021-11-26 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

Design, User Experience, and Usability: Understanding Users and Contexts

Author : Aaron Marcus
Publisher : Springer
ISBN 13 : 3319586408
Total Pages : 810 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Design, User Experience, and Usability: Understanding Users and Contexts by : Aaron Marcus

Download or read book Design, User Experience, and Usability: Understanding Users and Contexts written by Aaron Marcus and published by Springer. This book was released on 2017-06-28 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 10288, 10289, and 10290 constitutes the proceedings of the 6th International Conference on Design, User Experience, and Usability, DUXU 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCII 2017, in Vancouver, BC, Canada, in July 2017, jointly with 14 other thematically similar conferences. The total of 1228 papers presented at the HCII 2017 conferences were carefully reviewed and selected from 4340 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The total of 168 contributions included in the DUXU proceedings were carefully reviewed and selected for inclusion in this three-volume set. LNCS 10288: The 56 papers included in this volume are organized in topical sections on design thinking and design philosophy; aesthetics and perception in design; user experience evaluation methods and tools; user centered design in the software development lifecycle; DUXU education and training. LNCS 10289: The 56 papers included in this volume are organized in topical sections on persuasive and emotional design; mobile DUXU; designing the playing experience; designing the virtual, augmented and tangible experience; wearables and fashion technology. LNCS 10290: The 56 papers included in this volume are organized in topical sections on information design; understanding the user; DUXU for children and young users; DUXU for art, culture, tourism and environment; DUXU practice and case studies.

Big Data Analytics and Knowledge Discovery

Author : Min Song
Publisher : Springer Nature
ISBN 13 : 3030590658
Total Pages : 413 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Big Data Analytics and Knowledge Discovery by : Min Song

Download or read book Big Data Analytics and Knowledge Discovery written by Min Song and published by Springer Nature. This book was released on 2020-09-10 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume LNCS 12393 constitutes the papers of the 22nd International Conference Big Data Analytics and Knowledge Discovery which will be held online in September 2020. The 15 full papers presented together with 14 short papers plus 1 position paper in this volume were carefully reviewed and selected from a total of 77 submissions. This volume offers a wide range to following subjects on theoretical and practical aspects of big data analytics and knowledge discovery as a new generation of big data repository, data pre-processing, data mining, text mining, sequences, graph mining, and parallel processing.

Neural Information Processing

Author : Long Cheng
Publisher : Springer
ISBN 13 : 303004212X
Total Pages : 716 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Neural Information Processing by : Long Cheng

Download or read book Neural Information Processing written by Long Cheng and published by Springer. This book was released on 2018-12-03 with total page 716 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The 4th volume, LNCS 11304, is organized in topical sections on feature selection, clustering, classification, and detection.

Security, Privacy and Data Analytics

Author : Udai Pratap Rao
Publisher : Springer Nature
ISBN 13 : 9819935695
Total Pages : 424 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Security, Privacy and Data Analytics by : Udai Pratap Rao

Download or read book Security, Privacy and Data Analytics written by Udai Pratap Rao and published by Springer Nature. This book was released on 2023-09-19 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes refereed proceedings of the International Conference on Security, Privacy and Data Analytics, ISPDA 2022. The volume covers topics, including big data and analytics, cloud security and privacy, data intelligence, hardware security, network security, blockchain technology and distributed ledger, machine learning for security, and many others. The volume includes novel contributions and the latest developments from researchers across industry and academia working in security, privacy, and data analytics from technological and social perspectives. This book will emerge as a valuable reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners across the globe.

Machine Learning and Data Analytics for Solving Business Problems

Author : Bader Alyoubi
Publisher : Springer Nature
ISBN 13 : 3031184831
Total Pages : 214 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Machine Learning and Data Analytics for Solving Business Problems by : Bader Alyoubi

Download or read book Machine Learning and Data Analytics for Solving Business Problems written by Bader Alyoubi and published by Springer Nature. This book was released on 2022-12-15 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods – such as blockchain, big data, artificial intelligence, and cloud computing – so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems.

Recent Developments in Mechatronics and Intelligent Robotics

Author : Kevin Deng
Publisher : Springer
ISBN 13 : 3030002144
Total Pages : 1280 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Recent Developments in Mechatronics and Intelligent Robotics by : Kevin Deng

Download or read book Recent Developments in Mechatronics and Intelligent Robotics written by Kevin Deng and published by Springer. This book was released on 2018-10-04 with total page 1280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of proceedings of the International Conference on Mechatronics and Intelligent Robotics (ICMIR2018), held in Kunming, China during May 19–20, 2018. It consists of 155 papers, which have been categorized into 6 different sections: Intelligent Systems, Robotics, Intelligent Sensors & Actuators, Mechatronics, Computational Vision and Machine Learning, and Soft Computing. The volume covers the latest ideas and innovations both from the industrial and academic worlds, as well as shares the best practices in the fields of mechanical engineering, mechatronics, automatic control, IOT and its applications in industry, electrical engineering, finite element analysis and computational engineering. The volume covers key research outputs, which delivers a wealth of new ideas and food for thought to the readers.

MOBIMEDIA 2020

Author : Lin Yun
Publisher : European Alliance for Innovation
ISBN 13 : 1631902717
Total Pages : 1259 pages
Book Rating : 4.6/5 (319 download)

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Book Synopsis MOBIMEDIA 2020 by : Lin Yun

Download or read book MOBIMEDIA 2020 written by Lin Yun and published by European Alliance for Innovation. This book was released on 2020-11-19 with total page 1259 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are delighted to introduce the proceedings of the 13th edition of the 2020 European Alliance for Innovation (EAI) International Conference on Mobile Multimedia Communications (MOBIMEDIA). This conference has brought researchers, developers and practitioners around the world who are leveraging and developing multimedia coding, mobile communications and networking fields. Developing and leveraging multimedia coding, mobile communications and networking fields requires adopting an interdisciplinary approach where multimedia, networking and physical layer issues are addressed jointly. Basic theories, key technologies and Artificial Intelligence for next-generations wireless communications，intelligent technologies for subspace learning and clustering of high-dimensional data, security and safety, communication networks and coding analysis, electromagnetic and media access control, D2D and IoT, multimedia platform and analysis, new energy and smart city, vision and images analysis, systems and applications, case studies and prediction and educational application are research challenges that need to be carefully examined when designing new mobile media architectures. We also need to put a great effort in designing applications that take into account the way the user perceives the overall quality of the provided service. Within this scope, the MOBIMEDIA 2020 was intended to provide a unique international forum for researchers from industry and academia to study new technologies, applications and standards. Original unpublished contributions are solicited that can improve the knowledge and practice in the integrated design of efficient technologies and the relevant provision of advanced mobile multimedia applications.