A privacy-protecting coupon system
Liqun Chen, Matthias Enzmann, et al.
FC 2005
This paper reviews recent advances in supervised learning with a focus on two most important issues: performance and efficiency. Performance addresses the generalization capability of a learning machine on randomly chosen samples that are not included in a training set. Efficiency deals with the complexity of a learning machine in both space and time. As these two issues are general to various learning machines and learning approaches, we focus on a special type of adaptive learning systems with a neural architecture. We discuss four types of learning approaches: training an individual model; combinations of several well-trained models; combinations of many weak models; and evolutionary computation of models. We explore advantages and weaknesses of each approach and their interrelations, and we pose open questions for possible future research.
Liqun Chen, Matthias Enzmann, et al.
FC 2005
Anupam Gupta, Viswanath Nagarajan, et al.
Operations Research
Donald Samuels, Ian Stobert
SPIE Photomask Technology + EUV Lithography 2007
Elliot Linzer, M. Vetterli
Computing