Data Collection, Knowledge Discovery, Metrics and Publishing

AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

Ontotext

APRIL 4, 2019

They have different metrics for judging whether some content is interesting or not. Milena Yankova : What we did for the BBC in the previous Olympics was that we helped journalists publish their reports faster. Economy.bg: But doesn’t this algorithm put us in an information bubble by filtering the content for us?

Recreation/Entertainment

Recreation/Entertainment Testing Enterprise Knowledge Discovery

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. Quinlan, J.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Data Leaders Brief

AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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