000 05295nam a22006015i 4500
001 978-3-031-46549-9
003 DE-He213
005 20250516160027.0
007 cr nn 008mamaa
008 240424s2024 sz | s |||| 0|eng d
020 _a9783031465499
_9978-3-031-46549-9
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aArtificial Intelligence and Visualization: Advancing Visual Knowledge Discovery
_h[electronic resource] /
_cedited by Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Banissi.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXXI, 503 p. 280 illus., 258 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1126
505 0 _aVisualizing 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.
520 _aThis 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. .
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aArtificial intelligence.
650 0 _aEngineering
_xData processing.
650 1 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence.
650 2 4 _aData Engineering.
700 1 _aKovalerchuk, Boris.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aNazemi, Kawa.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aAndonie, Răzvan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aDatia, Nuno.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBanissi, Ebad.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031465482
776 0 8 _iPrinted edition:
_z9783031465505
776 0 8 _iPrinted edition:
_z9783031465512
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1126
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-46549-9
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c274883
_d274882