Open AccessRademacher Complexity in Simplex/l∞ Set
Author(s) -
YenShen Lu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1827/1/012145
Subject(s) - artificial neural network , computer science , set (abstract data type) , path (computing) , simplex , computational complexity theory , sample complexity , artificial intelligence , theoretical computer science , algorithm , mathematics , combinatorics , computer network , programming language
When the size of the neural network is too large, calculating the bound of neural network is a difficult problem. Therefore, “size-independent” is what needs to look for here. This paper follows the path of “Size-Independent Sample Complexity of Neural Network ”, and tries to get a better expression of Rademacher Complexity of neural networks.