Title :
The relevance vector machine technique for channel equalization application
Author :
Chen, S. ; Gunn, S.R. ; Harris, C.J.
Author_Institution :
Dept. of Electron. & Comput. Sci., Southampton Univ., UK
fDate :
11/1/2001 12:00:00 AM
Abstract :
The relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique
Keywords :
Gaussian noise; equalisers; learning automata; white noise; Bayesian equalizer; channel equalization; kernel representation; optimal performance; relevance vector machine technique; support vector machine technique; Bayesian methods; Communication channels; Design optimization; Equalizers; Kernel; Machine learning; Neural networks; Statistical learning; Support vector machine classification; Support vector machines;
Journal_Title :
Neural Networks, IEEE Transactions on
