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Titlebook: Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery; Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi Book 2024 The

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發(fā)表于 2025-3-21 18:29:12 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery
影響因子2023Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi
視頻videohttp://file.papertrans.cn/163/162337/162337.mp4
發(fā)行地址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
學科分類Studies in Computational Intelligence
圖書封面Titlebook: Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery;  Boris Kovalerchuk,Kawa Nazemi,Ebad Banissi Book 2024 The
影響因子.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 Vis
Pindex Book 2024
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Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery978-3-031-46549-9Series ISSN 1860-949X Series E-ISSN 1860-9503
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發(fā)表于 2025-3-22 07:56:40 | 只看該作者
Boris Kovalerchuk,Kawa Nazemi,Ebad BanissiProvides recent research on Artificial Intelligence, Visualization, Visual Knowledge Discovery, and Visual Analytics.Is devoted to AI and Visualization‘for advancing Visual Knowledge Discover.Contains
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Factorization and Riccati Equationsss. Decision Trees (DTs) are essential in machine learning because they are used to understand many black box ML models including Deep Learning models. In this research, two new methods for creation and enhancement with complete visualizing Decision Trees as understandable models are suggested. Thes
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Factorization of Measurable Matrix Functionsdesigned for numeric data. This work focuses on developing numeric coding schemes for non-numeric attributes for ML algorithms to support accurate and explainable ML models, methods for lossless visualization of n-D non-numeric categorical data with visual rule discovery in these visualizations, and
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