Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part IV

The European Conference on Machine Learning and Knowledge Discovery in Databases,ECML PKDD 2022, held in Grenoble, France, had 236 full papers and 17 Demo Track contributions, covering various topics in machine learning and knowledge discovery. The papers were reviewed and selected from 1060 submissions.

Format: Paperback / softback
Length: 641 pages
Publication date: 17 March 2023
Publisher: Springer International Publishing AG

The multi-volume set LNAI 13713 until 13718, comprising the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022), held in Grenoble, France, in September 2022, represents a comprehensive compilation of cutting-edge research and insights. This esteemed conference, attended by esteemed scholars and experts from around the world, witnessed the presentation of 236 full papers, meticulously reviewed and selected from a staggering 1060 submissions. In addition to the full papers, the proceedings feature 17 captivating Demo Track contributions, showcasing the latest advancements in machine learning and knowledge discovery.

The volumes in this set are organized into topical sections, providing a structured framework for the exploration of diverse areas within the field. Part I delves into clustering and dimensionality reduction, anomaly detection, interpretability and explainability, ranking and recommender systems, transfer and multitask learning, and more. Part II explores networks and graphs, encompassing knowledge graphs, social network analysis, graph neural networks, natural language processing and text mining, conversational systems, and more. Part III delves into deep learning, robust and adversarial machine learning, generative models, computer vision, meta-learning, neural architecture search, and more. Part IV explores reinforcement learning, multi-agent reinforcement learning, bandits and online learning, active and semi-supervised learning, private and federated learning, and more. Part V explores supervised learning, probabilistic inference, optimal transport, optimization, quantum, hardware, sustainability, and more. Part VI focuses on time series, financial machine learning, applications, transportation, and demo track.

Each section is meticulously curated to cover the most relevant and groundbreaking developments in machine learning and knowledge discovery, making this set an invaluable resource for researchers, practitioners, and students alike. The contributions presented in these proceedings showcase the state-of-the-art techniques, algorithms, and applications that are driving innovation in this field. The conference's rich networking opportunities and vibrant discussions further enriched the intellectual discourse, fostering collaborations and advancing the frontiers of knowledge.

In conclusion, the multi-volume set LNAI 13713 until 13718 serves as a testament to the remarkable achievements and advancements in machine learning and knowledge discovery. It represents a treasure trove of knowledge, providing a comprehensive overview of the latest research and trends in this dynamic domain. This set is a must-have for anyone interested in staying at the forefront of this rapidly evolving field.

Weight: 1038g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031264115
Edition number: 1st ed. 2023