A1062
Title: Classification with deep neural networks
Authors: Lei Shi - Fudan University (China) [presenting]
Zihan Zhang - Fudan University and City University of Hong Kong (China)
Dingxuan Zhou - University of Sydney (Australia)
Abstract: Classification with deep neural networks (DNNs) has made impressive advancements in various learning tasks. Due to the unboundedness of the target function, generalization analysis for DNN classifiers with logistic loss remains scarce. Recent progress in establishing a unified framework of generalization analysis for both bounded and unbounded target functions is reported. The analysis is based on a novel oracle-type inequality, which enables us to deal with the boundedness restriction of the target function. In particular, for logistic classifiers trained by deep, fully connected neural networks, the optimal convergence rates are obtained only by requiring the H\"{o}lder smoothness of the conditional probability. Under certain circumstances, such as when decision boundaries are smooth and the two classes are separable, the derived convergence rates can be independent of the input dimension.
