A dynamic interaction between machine learning and the philosophy of science
Minds and Machines 14 (4):539-549 (2004)
| Abstract | The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science. | |||||||||
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Abraham Meidan & Boris Levin (2002). Choosing From Competing Theories in Computerised Learning. Minds and Machines 12 (1):119-129.
Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko (2005). Argument Based Machine Learning Applied to Law. Artificial Intelligence and Law 13 (1):53-73.
Kevin B. Korb (2004). Introduction: Machine Learning as Philosophy of Science. Minds and Machines 14 (4):433-440.
Donald Gillies & Yuxin Zheng (2001). Dynamic Interactions with the Philosophy of Mathematics. Theoria 16 (3):437-459.
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