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european-conference-on-machine-learning-and-principles-and-practice-of-knowledge-discovery

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Use when targeting European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for AI/data mining.

Computer-Science-Conference-Skills/skills/european-conference-on-machine-learning-and-principles-and-practice-of-knowledge-discovery/SKILL.md

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# European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

## Conference positioning

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) is a top computer-science conference venue for European ML and knowledge-discovery research across methods, mining, applications, and data-centric AI. It rewards a solid ML or data-mining paper positioned for a broad European AI/data audience. Treat this skill as a **fit / venue-selection / re-framing** tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.

Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in `../../resources/conference-roster.md` and `../../resources/official-source-map.md`.

## When to trigger

- The author names ECML PKDD / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases as the target venue.
- A manuscript in European ML and knowledge-discovery research across methods needs a conference-fit read before being formatted or submitted.
- The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
- The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.
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