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.|
|Keywords||No keywords specified (fix it)|
|Categories||No categories specified (fix it)|
|Through your library||Configure|
Similar books and articles
Chris Thornton (1997). Brave Mobots Use Representation: Emergence of Representation in Fight-or-Flight Learning. 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 downloads9 ( #114,188 of 549,196 )
Recent downloads (6 months)1 ( #63,397 of 549,196 )
How can I increase my downloads?