David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Minds and Machines 14 (4):539-549 (2004)
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
|Keywords||Computer Science Philosophy of Mind Artificial Intelligence Systems Theory, Control Interdisciplinary Studies|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Chris Thornton (1997). Brave Mobots Use Representation: Emergence of Representation in Fight-or-Flight Learning. [REVIEW] Minds and Machines 7 (4):475-494.
S. Russell (1991). Inductive Learning by Machines. Philosophical Studies 64 (October):37-64.
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.
Added to index2009-01-28
Total downloads44 ( #95,201 of 1,907,046 )
Recent downloads (6 months)15 ( #42,879 of 1,907,046 )
How can I increase my downloads?