Gómez David, Rojas Alfonso
Telematics Engineering Department, Polytechnical University of Catalonia, Barcelona 08034, Spain
Neural Comput. 2016 Jan;28(1):216-28. doi: 10.1162/NECO_a_00793. Epub 2015 Nov 24.
A sizable amount of research has been done to improve the mechanisms for knowledge extraction such as machine learning classification or regression. Quite unintuitively, the no free lunch (NFL) theorem states that all optimization problem strategies perform equally well when averaged over all possible problems. This fact seems to clash with the effort put forth toward better algorithms. This letter explores empirically the effect of the NFL theorem on some popular machine learning classification techniques over real-world data sets.
为了改进诸如机器学习分类或回归等知识提取机制，已经开展了大量研究。相当出乎意料的是，无免费午餐（NFL）定理指出，在所有可能的问题上进行平均时，所有优化问题策略的表现都同样出色。这一事实似乎与为改进算法所付出的努力相冲突。本文通过实证研究了NFL定理对一些流行的机器学习分类技术在真实数据集上的影响。
