We consider several complexity measures which capture the difficulty of learning under the i.i.d. assumption. Among these measures are growth function and VC dimension, covering number and fat-shattering dimension, and Rademacher complexity from statistical learning theory. Relationships among these complexity measures, their connection to learning, and tools for bounding them are provided. For each complexity measure, a uniform upper bound on the generalization error of classification problems is presented.
|Title of host publication||Measures of Complexity|
|Subtitle of host publication||Festschrift for Alexey Chervonenkis|
|Publisher||Springer International Publishing|
|Number of pages||18|
|Publication status||Published - 5 Oct 2015|