Jonathan R.M. Hosking
Journal of Econometrics
Data mining can be regarded as a collection of methods for drawing inferences from data. The aims of data mining, and some of its methods, overlap with those of classical statistics. However, there are some philosophical and methodological differences. We examine these differences, and we describe three approaches to machine learning that have developed largely independently: classical statistics, Vapnik's statistical learning theory, and computational learning theory. Comparing these approaches, we conclude that statisticians and data miners can profit by studying each other's methods and using a judiciously chosen combination of them.
Jonathan R.M. Hosking
Journal of Econometrics
Michael Kapralov, Sanjeev Khanna, et al.
SODA 2015
Luca Trevisan, Gregory B. Sorkin, et al.
SIAM Journal on Computing
Christos H. Papadimitriou, Prabhakar Raghavan, et al.
FOCS 1994