caa a22 4500 605477833 CHVBK 20210128100403.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-015-5525-9 doi (NATIONALLICENCE)springer-10.1007/s10994-015-5525-9 Stochastic feature mapping for PAC-Bayes classification [Elektronische Daten] [Xiong Li, Bin Wang, Yuncai Liu, Tai Lee] Hidden information derived from probabilistic generative models of data distributions can be used to construct features for discriminative classifiers. This observation has motivated the development of approaches that attempt to couple generative and discriminative models together for classification. However, existing approaches typically feed features derived from generative models to discriminative classifiers, and do not refine the generative models or the feature mapping functions based on classification results. In this paper, we propose a coupling mechanism developed under the PAC-Bayes framework that can fine-tune the generative models and the feature mapping functions iteratively to improve the classifier's performance. In our approach, a stochastic feature mapping, which is a function over the random variables of a generative model, is derived to generate feature vectors for a stochastic classifier. We construct a stochastic classifier over the feature mapping and derive the PAC-Bayes generalization bound for the classifier, for both supervised and semi-supervised learning. This allows us to jointly learn the feature mapping and the classifier by minimizing the bound with an EM-like iterative algorithm using labeled and unlabeled data. The resulting framework integrates the learning of the discriminative classifier and the generative model and allows iterative fine-tuning of the generative models, and the feedforward feature mappings based on task performance feedback. Our experiments show, in three distinct applications, this new framework produces a general classification tool with state-of-the-art performance. The Author(s), 2015 Stochastic feature mapping nationallicence PAC-Bayes generalization bound nationallicence Hybrid generative-discriminative classification nationallicence Li Xiong Department of Automation, Shanghai Jiao Tong University, Shanghai, China aut Wang Bin College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China aut Liu Yuncai Department of Automation, Shanghai Jiao Tong University, Shanghai, China aut Lee Tai Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 5-33 0885-6125 101:1-3<5 2015 101 10994 https://doi.org/10.1007/s10994-015-5525-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5525-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Xiong Department of Automation, Shanghai Jiao Tong University, Shanghai, China aut NATIONALLICENCE 700 1- Wang Bin College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China aut NATIONALLICENCE 700 1- Liu Yuncai Department of Automation, Shanghai Jiao Tong University, Shanghai, China aut NATIONALLICENCE 700 1- Lee Tai Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 5-33 0885-6125 101:1-3<5 2015 101 10994