LGDLFeb 24, 2014

Open science in machine learning

arXiv:1402.6013v114 citations
Originality Synthesis-oriented
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This addresses the problem of limited accessibility and reproducibility in machine learning research for the broader research community, though it is incremental as it builds on existing repository concepts.

The paper introduces OpenML and mldata, open science platforms that provide access to machine learning data, software, and results to facilitate study and application, enabling researchers to share and compare experimental outcomes.

We present OpenML and mldata, open science platforms that provides easy access to machine learning data, software and results to encourage further study and application. They go beyond the more traditional repositories for data sets and software packages in that they allow researchers to also easily share the results they obtained in experiments and to compare their solutions with those of others.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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