Markus Bayer: Deep Learning in Textual Low-Data Regimes for Cybersecurity, Kartoniert / Broschiert
Deep Learning in Textual Low-Data Regimes for Cybersecurity
(soweit verfügbar beim Lieferanten)
- Verlag:
- Springer Vieweg, 08/2025
- Einband:
- Kartoniert / Broschiert
- Sprache:
- Englisch
- ISBN-13:
- 9783658487775
- Artikelnummer:
- 12457301
- Umfang:
- 376 Seiten
- Gewicht:
- 486 g
- Maße:
- 210 x 148 mm
- Stärke:
- 21 mm
- Erscheinungstermin:
- 21.8.2025
- Hinweis
-
Achtung: Artikel ist nicht in deutscher Sprache!
Klappentext
Introduction.- Research Design.- Findings.- Discussion.- Conclusion.- Information Overload in Crisis Management: Bilingual Evaluation of Embedding Models for Clustering Social Media Posts in Emergencies.- ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios.- A Survey on Data Augmentation for Text Classification.- Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers.- Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter.- CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain.- Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence.- XAI-Attack: Utilizing Explainable AI to Find Incorrectly Learned Patterns for Black-Box Adversarial Example Creation.
