KI-Beratung Beta
Vor Ort
Merkzettel Warenkorb
Produktbild: Intelligent Data Engineering and Automated Learning – IDEAL 2026
Band 17196 Vorbesteller

Intelligent Data Engineering and Automated Learning – IDEAL 2026 27th International Conference, Manchester, UK, November 19–21, 2026, Proceedings, Part II

Aus der Reihe Lecture Notes in Computer Science

98,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

This two-volume set constitutes the proceedings of the 27th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2026, which was held in Manchester, UK, during November 19-21, 2026.

The 73 full papers included in this volume were carefully reviewed and selected from 108...

Beschreibung

This two-volume set constitutes the proceedings of the 27th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2026, which was held in Manchester, UK, during November 19-21, 2026.

The 73 full papers included in this volume were carefully reviewed and selected from 108 submissions. The core themes of IDEAL 2026 included Big Data challenges, Machine Learning, Deep Learning, Data Mining, Information Retrieval and Management, Bio-/Neuro-Informatics, Bio-Inspired Models, Agents and Hybrid Intelligent Systems, Real-world Applications of Intelligence Techniques and AI. This year, IDEAL teamed up with WSOM+ (Workshop on Self-Organising Maps and beyond), a long-standing workshop dedicated to self-organising learning systems.

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

01.01.2027
Abbildungen

XII, 212 p. 56 illus.
Herausgeber Hujun Yin + weitere

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

01.01.2027
Abbildungen

XII, 212 p. 56 illus.
Herausgeber
Verlag Springer
Seitenzahl

212
Maße (B/H)

15,5/23,5 cm
Sprache

Englisch
ISBN

978-3-032-45369-3
Herstelleradresse

Springer International Publishing AG
Gewerbestr. 11
6330 Cham
Schweiz
Url: www.springer.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Intelligent Data Engineering and Automated Learning – IDEAL 2026

  • .- Topology-Guided Localized Color Augmentation with Growing Neural Gas.

    .- Benchmarking Generative Topographic Mapping for Probabilistic Clustering and Interpretable Dimensionality Reduction.

    .- Theoretical Limits of Integer Quantization in LVQ: From Fano’s Inequality to Gradient Confusion.

    .- An Efficient Proximal Interpretation of Batch Self-Organizing Maps.

    .- Inspect and Explain: Counterfactual Explanations via Dominating Features in Prototype-based Models.

    .- Notes on GLVQ Convergence Without Smoothness Assumptions.

    .- Adam is a competitive multi-scale neighbour embedding optimizer.

    .- Implicit Word Sense Disambiguation in LLMs.

    .- Unsupervised Structure versus Diabetic Retinopathy Status in OCTA Radiomics: A Generative Topographic Mapping Study.

    .- Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations.

    .- Characterization of SOM Model Quality with Graph Property Measures.

    .- RSLVQ revisited: robust soft learning vector quantization for high-dimensional data.

    .- Evaluation of Classification Uncertainty in Learning Vector Quantization: Current Trends and Approaches including Evidence-theoretic Aspects.

    .-Localizing Dissimilarity Contributions in M³GLVQ.

    .- Lilypond: A Unified SOM Visualization Framework with Integrated Distortion Mapping.

    .- Repair, Then Trust: Feature-Salience Detection and Repair of Degenerate Models for Optimal Cluster Selection.

    .- Vector Quantization for Uncertainty Quantification: The Quantized Infinitesimal Jackknife.

    .- Self-organizing map formation under the farthest first traversal constraint.