İktisadi ve İdari Yaklaşımlar Dergisi

Yazarlar: Gökhan KORKMAZ, Ergün EROĞLU

Konular:Sosyal Bilimler, Disiplinler Arası

DOI:10.47138/jeaa.780031

Anahtar Kelimeler:Model Complexity,Occam’s Razor,Popper’s Falsifiability,Statistical Learning Theory

Özet: Model complexity is one of the most important criteria for the success of models. In this study, the prominent approaches to controlling model complexity have been examined under headings. These are Occam’s razor, Popper’s falsifiability, and the statistical learning theory. Occam’s razor and Popper’s falsifiability in the control of model complexity, yes they provide a philosophical approach and they are also accepted. However, they do not provide a mathematical formulation on how to control model complexity. However, the statistical learning theory (aka VC theory) approach to the subject is not only at a philosophical level, but also introduces a new principle of risk minimization (structural risk minimization, YRM) by adding the VC coefficient to the empirical risk minimization (ARM) principle used in the models developed so far. . As a result, the VC theory developed by Vapnik and Chervonenkis as a control model, with its proven mathematical background and highly successful results, can be a good source of inspiration for model developers as the most consistent and reliable approach to the control of model complexity in today’s framework.

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