De Aller-Bedste Bøger - over 12 mio. danske og engelske bøger
Levering: 1 - 2 hverdage
Hjælp
Login
  1. Forside
  2. Bøger
  3. Business og læring
  4. Computer og IT
  5. Machine Learning and Knowledge Discovery in Databases: Research Track

Machine Learning and Knowledge Discovery in Databases: Research Track

af Danai Koutra

indgår i Lecture Notes in Artificial Intelligence serien

Machine Learning and Knowledge Discovery in Databases: Research Track af Danai Koutra
Bag om Machine Learning and Knowledge Discovery in Databases: Research Track

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: ¿Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ¿Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ¿Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: ¿Robustness; Time Series; Transfer and Multitask Learning. Part VI: ¿Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ¿Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Vis mere
  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031434204
  • Indbinding:
  • Paperback
  • Sideantal:
  • 804
  • Udgivet:
  • 18. september 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 155x43x235 mm.
  • Vægt:
  • 1194 g.
  • 8-11 hverdage.
  • 16. januar 2025
På lager

Normalpris

Abonnementspris

- Rabat på køb af fysiske bøger
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding

Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.

Bag om Danai Koutra

Vis alle bøger af Danai Koutra

Beskrivelse af Machine Learning and Knowledge Discovery in Databases: Research Track

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.
The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.
The volumes are organized in topical sections as follows:
Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering.Part II: ¿Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.
Part III: ¿Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.
Part IV: ¿Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.Part V: ¿Robustness; Time Series; Transfer and Multitask Learning.
Part VI: ¿Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.
¿Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Brugerbedømmelser af Machine Learning and Knowledge Discovery in Databases: Research Track



Andre købte også..
Find lignende bøger
Bogen Machine Learning and Knowledge Discovery in Databases: Research Track findes i følgende kategorier: