Visual Knowledge Discovery And Machine Learning

Author : Boris Kovalerchuk,Kawa Nazemi,Răzvan Andonie,Nuno Datia,Ebad Banissi
Publisher : Springer Nature
Page : 671 pages
File Size : 48,5 Mb
Release : 2022-06-04
Category : Technology & Engineering
ISBN : 9783030931193

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Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery by Boris Kovalerchuk,Kawa Nazemi,Răzvan Andonie,Nuno Datia,Ebad Banissi Pdf

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 Nature
Page : 510 pages
File Size : 40,6 Mb
Release : 2024-06-25
Category : Electronic
ISBN : 9783031465499

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Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery by Boris Kovalerchuk Pdf

Author : Boris Kovalerchuk
Publisher : Springer
Page : 317 pages
File Size : 46,9 Mb
Release : 2018-01-17
Category : Technology & Engineering
ISBN : 9783319730400

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Visual Knowledge Discovery and Machine Learning by Boris Kovalerchuk Pdf

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 : Simeon Simoff,Michael H. Böhlen,Arturas Mazeika
Publisher : Springer Science & Business Media
Page : 417 pages
File Size : 43,5 Mb
Release : 2008-07-18
Category : Computers
ISBN : 9783540710790

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Visual Data Mining by Simeon Simoff,Michael H. Böhlen,Arturas Mazeika Pdf

The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected. This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications.

Author : Gianmarco De Francisci Morales,Claudia Perlich,Natali Ruchansky,Nicolas Kourtellis,Elena Baralis,Francesco Bonchi
Publisher : Springer Nature
Page : 429 pages
File Size : 42,7 Mb
Release : 2023-09-16
Category : Computers
ISBN : 9783031434303

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track by Gianmarco De Francisci Morales,Claudia Perlich,Natali Ruchansky,Nicolas Kourtellis,Elena Baralis,Francesco Bonchi Pdf

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Author : Usama M. Fayyad,Georges G. Grinstein,Andreas Wierse
Publisher : Morgan Kaufmann
Page : 446 pages
File Size : 46,5 Mb
Release : 2002
Category : Computers
ISBN : 1558606890

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Information Visualization in Data Mining and Knowledge Discovery by Usama M. Fayyad,Georges G. Grinstein,Andreas Wierse Pdf

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 : Lior Rokach,Oded Maimon,Erez Shmueli
Publisher : Springer Nature
Page : 975 pages
File Size : 49,6 Mb
Release : 2023-08-17
Category : Computers
ISBN : 9783031246289

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Machine Learning for Data Science Handbook by Lior Rokach,Oded Maimon,Erez Shmueli Pdf

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 : Yuxiao Dong,Nicolas Kourtellis,Barbara Hammer,Jose A. Lozano
Publisher : Springer Nature
Page : 542 pages
File Size : 49,8 Mb
Release : 2021-09-09
Category : Computers
ISBN : 9783030865177

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track by Yuxiao Dong,Nicolas Kourtellis,Barbara Hammer,Jose A. Lozano Pdf

The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.

Author : Nuria Oliver,Fernando Pérez-Cruz,Stefan Kramer,Jesse Read,Jose A. Lozano
Publisher : Springer Nature
Page : 838 pages
File Size : 55,8 Mb
Release : 2021-09-09
Category : Computers
ISBN : 9783030864866

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Machine Learning and Knowledge Discovery in Databases. Research Track by Nuria Oliver,Fernando Pérez-Cruz,Stefan Kramer,Jesse Read,Jose A. Lozano Pdf

The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.

Author : Sakae Yamamoto,Hirohiko Mori
Publisher : Springer
Page : 760 pages
File Size : 54,6 Mb
Release : 2018-07-09
Category : Computers
ISBN : 9783319920436

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Human Interface and the Management of Information. Interaction, Visualization, and Analytics by Sakae Yamamoto,Hirohiko Mori Pdf

This two-volume set LNCS 10904 and 10905 constitutes the refereed proceedings of the 20th International Conference on Human Interface and the Management of Information, HIMI 2018, held as part of HCI International 2018 in Las Vegas, NV, USA, in July 2018.The total of 1170 papers and 195 posters included in the 30 HCII 2018 proceedings volumes was carefully reviewed and selected from 4373 submissions. The 56 papers presented in this volume were organized in topical sections named: information visualization; multimodal interaction; information in virtual and augmented reality; information and vision; and text and data mining and analytics.

Author : Dinh Phung,Vincent S. Tseng,Geoffrey I. Webb,Bao Ho,Mohadeseh Ganji,Lida Rashidi
Publisher : Springer
Page : 720 pages
File Size : 41,8 Mb
Release : 2018-06-18
Category : Computers
ISBN : 9783319930343

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Advances in Knowledge Discovery and Data Mining by Dinh Phung,Vincent S. Tseng,Geoffrey I. Webb,Bao Ho,Mohadeseh Ganji,Lida Rashidi Pdf

This three-volume set, LNAI 10937, 10938, and 10939, constitutes the thoroughly refereed proceedings of the 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, held in Melbourne, VIC, Australia, in June 2018. The 164 full papers were carefully reviewed and selected from 592 submissions. The volumes present papers focusing on new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications.

Author : Nuria Oliver,Fernando Pérez-Cruz,Stefan Kramer,Jesse Read,Jose A. Lozano
Publisher : Springer Nature
Page : 857 pages
File Size : 54,9 Mb
Release : 2021-09-10
Category : Computers
ISBN : 9783030865238

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Machine Learning and Knowledge Discovery in Databases. Research Track by Nuria Oliver,Fernando Pérez-Cruz,Stefan Kramer,Jesse Read,Jose A. Lozano Pdf

The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.

Author : Yuxiao Dong,Nicolas Kourtellis,Barbara Hammer,Jose A. Lozano
Publisher : Springer Nature
Page : 579 pages
File Size : 40,8 Mb
Release : 2021-09-09
Category : Computers
ISBN : 9783030865146

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track by Yuxiao Dong,Nicolas Kourtellis,Barbara Hammer,Jose A. Lozano Pdf

The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.

Author : Nuria Oliver,Fernando Pérez-Cruz,Stefan Kramer,Jesse Read,Jose A. Lozano
Publisher : Springer Nature
Page : 817 pages
File Size : 47,6 Mb
Release : 2021-09-09
Category : Computers
ISBN : 9783030865207

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Machine Learning and Knowledge Discovery in Databases. Research Track by Nuria Oliver,Fernando Pérez-Cruz,Stefan Kramer,Jesse Read,Jose A. Lozano Pdf

The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.

Author : Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook
Publisher : Springer Science & Business Media
Page : 375 pages
File Size : 52,6 Mb
Release : 2006-05-06
Category : Computers
ISBN : 9781846282843

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Advanced Methods for Knowledge Discovery from Complex Data by Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook Pdf

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.