Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice

Author:

Li DanORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Philosophy

Reference33 articles.

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2. Bontempi, G. (2019). The induction problem: A machine learning vindication argument. In N. Giuseppe, P. Panos, U. Renato, G. Giovanni, & S. Vincenzo (Eds.), Machine learning optimization, and data science. Lecture notes in computer science (pp. 232–43). Springer International Publishing. https://doi.org/10.1007/978-3-030-37599-7_20

3. Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231.

4. Broad, C. D. (1926). The philosophy of Francis Bacon: An address delivered at Cambridge on the occasion of the Bacon Tercentenary. Cambridge University Press.

5. Buolamwini, J. (2018). and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency, (pp. 77–91). PMLR. http://proceedings.mlr.press/v81/buolamwini18a.html.

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