Machine Learning and Knowledge Discovery in Databases
- Indbinding:
- Paperback
- Sideantal:
- 776
- Udgivet:
- 17. marts 2023
- Udgave:
- 23001
- Størrelse:
- 155x42x235 mm.
- Vægt:
- 1153 g.
- 8-11 hverdage.
- 7. december 2024
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- 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.
Beskrivelse af Machine Learning and Knowledge Discovery in Databases
The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.
The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions.
The volumes are organized in topical sections as follows:
Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning;
Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems;
Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search;
Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; .
Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability;
Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions.
The volumes are organized in topical sections as follows:
Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning;
Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems;
Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search;
Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; .
Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability;
Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
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Bogen Machine Learning and Knowledge Discovery in Databases findes i følgende kategorier:
- Business og læring > Computer og IT
- Databehandling og informationsteknologi > Informationsteknologi: generelle emner
- Databehandling og informationsteknologi > Databaser > Data mining
- Databehandling og informationsteknologi > Anvendt databehandling > Software til pædagogik, psykologi, sociologi og samfundsfag
- Databehandling og informationsteknologi > Informatik > Matematisk datateori > Matematik til informatikfag
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Ekspertsystemer og vidensbaserede systemer
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