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Author: Boris Kovalerchuk Publisher: Springer Nature ISBN: 3031465490 Category : Artificial intelligence Languages : en Pages : 512
Book Description
Zusammenfassung: This book continues a series of Springer publications devoted to the emerging field of Integrated Artificial Intelligence and Machine Learning with Visual Knowledge Discovery and Visual Analytics that combine advances in both fields. Artificial Intelligence and Machine Learning face long-standing challenges of explainability and interpretability that underpin trust. Such attributes are fundamental to both decision-making and knowledge discovery. Models are approximations and, at best, interpretations of reality that are transposed to algorithmic form. A visual explanation paradigm is critically important to address such challenges, as current studies demonstrate in salience analysis in deep learning for images and texts. Visualization means are generally effective for discovering and explaining high-dimensional patterns in all high-dimensional data, while preserving data properties and relations in visualizations is challenging. Recent developments, such as in General Line Coordinates, open new opportunities to address such challenges. This book contains extended papers presented in 2021 and 2022 at the International Conference on Information Visualization (IV) on AI and Visual Analytics, with 18 chapters from international collaborators. The book builds on the previous volume, published in 2022 in the Studies in Computational Intelligence. The current book focuses on the following themes: knowledge discovery with lossless visualizations, AI/ML through visual knowledge discovery with visual analytics case studies application, and visual knowledge discovery in text mining and natural language processing. The intended audience for this collection includes but is not limited to developers of emerging AI/machine learning and visualization applications, scientists, practitioners, and research students. It has multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery, visual analytics, and text and natural language processing. The book provides case examples for future directions in this domain. New researchers find inspiration to join the profession of the field of AI/machine learning through a visualization lens.
Author: Boris Kovalerchuk Publisher: Springer Nature ISBN: 3031465490 Category : Artificial intelligence Languages : en Pages : 512
Book Description
Zusammenfassung: This book continues a series of Springer publications devoted to the emerging field of Integrated Artificial Intelligence and Machine Learning with Visual Knowledge Discovery and Visual Analytics that combine advances in both fields. Artificial Intelligence and Machine Learning face long-standing challenges of explainability and interpretability that underpin trust. Such attributes are fundamental to both decision-making and knowledge discovery. Models are approximations and, at best, interpretations of reality that are transposed to algorithmic form. A visual explanation paradigm is critically important to address such challenges, as current studies demonstrate in salience analysis in deep learning for images and texts. Visualization means are generally effective for discovering and explaining high-dimensional patterns in all high-dimensional data, while preserving data properties and relations in visualizations is challenging. Recent developments, such as in General Line Coordinates, open new opportunities to address such challenges. This book contains extended papers presented in 2021 and 2022 at the International Conference on Information Visualization (IV) on AI and Visual Analytics, with 18 chapters from international collaborators. The book builds on the previous volume, published in 2022 in the Studies in Computational Intelligence. The current book focuses on the following themes: knowledge discovery with lossless visualizations, AI/ML through visual knowledge discovery with visual analytics case studies application, and visual knowledge discovery in text mining and natural language processing. The intended audience for this collection includes but is not limited to developers of emerging AI/machine learning and visualization applications, scientists, practitioners, and research students. It has multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery, visual analytics, and text and natural language processing. The book provides case examples for future directions in this domain. New researchers find inspiration to join the profession of the field of AI/machine learning through a visualization lens.
Author: Boris Kovalerchuk Publisher: Springer Nature ISBN: 3030931196 Category : Technology & Engineering Languages : en Pages : 671
Book Description
This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.
Author: Boris Kovalerchuk Publisher: Springer ISBN: 3319730401 Category : Technology & Engineering Languages : en Pages : 332
Book Description
This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.
Author: Thoralf Reis Publisher: Springer Nature ISBN: 303068007X Category : Computers Languages : en Pages : 213
Book Description
This book constitutes the thoroughly refereed post-workshop proceedings of the AVI 2020 Workshop on Road Mapping Infrastructures for Artificial Intelligence Supporting Advanced Visual Big Data Analysis, AVI-BDA 2020, held in Ischia, Italy, in June 2020, and the Second Italian Workshop on Visualization and Visual Analytics, held in Ischia, Italy, in September 2020. The 14 regular papers in this volume present topics such as big data collection, management and curation; big data analytics; big data interaction and perception; big data insight and effectuation; configuration and management of big data storage and compute infrastructures, services, and tools; advanced visual interaction in big data applications; user empowerment and meta design in big data applications; prediction and automation of big data analysis workflows; as well as data visualization; information visualization; visual analytics; infographics; and design.
Author: Usama M. Fayyad Publisher: Morgan Kaufmann ISBN: 9781558606890 Category : Computers Languages : en Pages : 446
Book Description
This text surveys research from the fields of data mining and information visualisation and presents a case for techniques by which information visualisation can be used to uncover real knowledge hidden away in large databases.
Author: Simeon Simoff Publisher: Springer ISBN: 3540710809 Category : Computers Languages : en Pages : 417
Book Description
Visual Data Mining—Opening the Black Box Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. Thenatureofwhatmakesaruleinterestingtoauserhasbeendiscussed 1 widely but most agree that it is a subjective quality based on the practical u- fulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can in?uence the behavior of the system. There have been some impressive research activities over the past few years but the question to be asked is why is visual data mining only now being - vestigated commercially? Certainly, there have been arguments for visual data 2 mining for a number of years – Ankerst and others argued in 2002 that current (autonomous and opaque) analysis techniques are ine?cient, as they fail to - rectly embed the user in dataset exploration and that a better solution involves the user and algorithm being more tightly coupled. Grinstein stated that the “current state of the art data mining tools are automated, but the perfect data mining tool is interactive and highly participatory,” while Han has suggested that the “data selection and viewing of mining results should be fully inter- tive, the mining process should be more interactive than the current state of the 2 art and embedded applications should be fairly automated . ” A good survey on 3 techniques until 2003 was published by de Oliveira and Levkowitz .
Author: Jude Hemanth Publisher: Springer ISBN: 3030257975 Category : Technology & Engineering Languages : en Pages : 319
Book Description
This book presents the fundamentals and advances in the field of data visualization and knowledge engineering, supported by case studies and practical examples. Data visualization and engineering has been instrumental in the development of many data-driven products and processes. As such the book promotes basic research on data visualization and knowledge engineering toward data engineering and knowledge. Visual data exploration focuses on perception of information and manipulation of data to enable even non-expert users to extract knowledge. A number of visualization techniques are used in a variety of systems that provide users with innovative ways to interact with data and reveal patterns. A variety of scalable data visualization techniques are required to deal with constantly increasing volume of data in different formats. Knowledge engineering deals with the simulation of the exchange of ideas and the development of smart information systems in which reasoning and knowledge play an important role. Presenting research in areas like data visualization and knowledge engineering, this book is a valuable resource for students, scholars and researchers in the field. Each chapter is self-contained and offers an in-depth analysis of real-world applications. It discusses topics including (but not limited to) spatial data visualization; biomedical visualization and applications; image/video summarization and visualization; perception and cognition in visualization; visualization taxonomies and models; abstract data visualization; information and graph visualization; knowledge engineering; human–machine cooperation; metamodeling; natural language processing; architectures of database, expert and knowledge-based systems; knowledge acquisition methods; applications, case studies and management issues: data administration issues and knowledge; tools for specifying and developing data and knowledge bases using tools based on communication aspects involved in implementing, designing and using KBSs in cyberspace; Semantic Web.
Author: Magy Seif El-Nasr Publisher: Oxford University Press ISBN: 019289787X Category : Computers Languages : en Pages : 414
Book Description
"Game Data Science delivers a thorough introduction to this new domain and serves as a definitive guide to the methods and practices of computer science, analytics, and data science as applied to video games. It is the ideal resource for professional learners and students seeking to understand how data science is used within the game development and production cycle, as well as within the interdisciplinary field of games research." -- back cover.
Author: Lior Rokach Publisher: Springer Nature ISBN: 3031246284 Category : Computers Languages : en Pages : 975
Book Description
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
Author: Boris Kovalerchuk
Author: Boris Kovalerchuk
Author: Boris Kovalerchuk
Author: Boris Kovalerchuk
Author: Thoralf Reis
Author: Usama M. Fayyad
Author: Simeon Simoff
Author: Jude Hemanth
Author: Magy Seif El-Nasr
Author: Lior Rokach
Author: Nikhil Pal