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Social Media Modeling and Computing
This timely text/reference presents the latest advances in various aspects of social media modeling and social media computing research. Gathering together superb research from a range of established international conferences and workshops, the editors coherently organize and present each of the topics in relation to the basic principles and practices of social media modeling and computing. Individual chapters can be also be used as self-contained references on the material covered. Topics and features: presents contributions from an international selection of preeminent experts in the field; discusses topics on social-media content analysis; examines social-media system design and analysis, and visual analytic tools for event analysis; investigates access control for privacy and security issues in social networks; describes emerging applications of social media, for music recommendation, automatic image annotation, and the analysis and improvement of photo-books.
Online Portfolio Selection
With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strateg...
Federated Learning for Future Intelligent Wireless Networks
Federated Learning for Future Intelligent Wireless Networks Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. Readers will explore a wide range of topics that show how...
Metric Learning
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data....
Advances in Multimedia Modeling
This two-volume proceedings constitutes the refereed papers of the 17th International Multimedia Modeling Conference, MMM 2011, held in Taipei, Taiwan, in January 2011. The 51 revised regular papers, 25 special session papers, 21 poster session papers, and 3 demo session papers, were carefully reviewed and selected from 450 submissions. The papers are organized in topical sections on audio, image video processing, coding and compression; media content browsing and retrieval; multi-camera, multi-view, and 3D systems; multimedia indexing and mining; multimedia content analysis; multimedia signal processing and communications; and multimedia applications. The special session papers deal with content analysis for human-centered multimedia applications; large scale rich media data management; multimedia understanding for consumer electronics; image object recognition and compression; and interactive image and video search.
Computer Vision
Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications. This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI. Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners.
Algorithmic Learning Theory
This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6–8, 2010. The conference was co-located with the 13th - ternational Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ?ve invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Austr...
Advances in Multilingual and Multimodal Information Retrieval
The eighth campaign of the Cross Language Evaluation Forum (CLEF) for - ropean languages was held from January to September 2007. There were seven distinct evaluation tracks in CLEF 2007, designed to test the performance of a wide range of multilingual information access systems or system components. CLEF is by now an established international evaluation initiative and, in 2007, 81 groups from all over the world submitted results for one or more of the di?erent evaluation tracks. Full details regarding the design of the tracks, the methodologies used for evaluation, and the results obtained by the participants can be found in the di?erent sections of these proceedings. As always the results of the campaign were reported and discussed at the annual workshop, held in Budapest, Hungary, 19-21 September, immediately following the eleventh European Conference on Digital Libraries. The workshop playsanimportantrolebyprovidingtheopportunityforallthe groupsthathave participated in the evaluation campaign to get together to compare approaches and exchange ideas.
Database Systems for Advanced Applications
This two volume set LNCS 5981 and LNCS 5982 constitutes the refereed proceedings of the 15th International Conference on Database Systems for Advanced Applications, DASFAA 2010, held in Tsukuba, Japan, in April 2010. The 39 revised full papers and 16 revised short papers presented together with 3 invited keynote papers, 22 demonstration papers, 6 industrial papers, and 2 keynote talks were carefully reviewed and selected from 285 submissions. The papers of the first volume are organized in topical sections on P2P-based technologies, data mining technologies, XML search and matching, graphs, spatialdatabases, XML technologies, time series and streams, advanced data mining, query processing, Web, sensor networks and communications, information management, as well as communities and Web graphs. The second volume contains contributions related to trajectories and moving objects, skyline queries, privacy and security, data streams, similarity search and event processing, storage and advanced topics, industrial, demo papers, and tutorials and panels.
Deep Learning for Image Processing Applications
Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.
