BBAuthors: Luo, Zhaojing; Advisor: -; Participants: Cai, Shaofeng; Chen, Gang; Gao, Jinyang; Lee, Wang-Chien; Ngiam, Kee Yuan; Zhang, Meihui (2019)
Deep Learning and Machine Learning models have recently been shown to be effective in many real world applications. While these models achieve increasingly better predictive performance, their structures have also become much more complex. A common and difficult problem for complex models is overfitting. Regularization is used to penalize the complexity of the model in order to avoid overfitting. However, in most learning frameworks, regularization function is usually set with some hyper-parameters where the best setting is difficult to find. In this paper, we propose an adaptive regularization method, as part of a large end-to-end healthcare data analytics software stack, which effectively addresses the above difficulty. First, we propose a general adaptive regularization method ba... 
