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Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery de Boris Kovalerchuk

Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery: Studies in Computational Intelligence

Editat de Boris Kovalerchuk, Kawa Nazemi, R¿zvan Andonie, Nuno Datia, Ebad Banissi
en Limba Engleză Paperback – 25 apr 2025
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.
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Specificații

ISBN-13: 9783031465512
ISBN-10: 3031465512
Pagini: 528
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.89 kg
Editura: Springer
Seria Studies in Computational Intelligence

Cuprins

Visualizing the Unseen: Unleashing Knowledge Discovery with Lossless Visualizations.- Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling.- Full High-dimensional Intelligible Learning In 2-D Lossless Visualization Space.- Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization.- Parallel Coordinates for Discovery of Interpretable Machine Learning Models.- Visual Knowledge Discovery with General Line Coordinates.- Unveiling Insights: Empowering AI/ML through Visual Knowledge Discovery.

Textul de pe ultima copertă

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. 

Caracteristici

Provides recent research on Artificial Intelligence, Visualization, Visual Knowledge Discovery, and Visual Analytics Is devoted to AI and Visualization for advancing Visual Knowledge Discover Contains extended papers from the International Conference on Information Visualization related to AI