Failure diagnosis with incomplete information in cable networks
Yun Mao, Hani Jamjoom, et al.
CoNEXT 2006
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.
Yun Mao, Hani Jamjoom, et al.
CoNEXT 2006
Thomas M. Cheng
IT Professional
Oliver Bodemer
IBM J. Res. Dev
Yao Qi, Raja Das, et al.
ISSTA 2009