@inbook{a66bdff45ccf48dc9d92b9b048b81e7e,

title = "A greedy training algorithm for sparse least-squares support vector machines",

abstract = "Suykens et al. [1] describes a form of kernel ridge regression known as the least-squares support vector machine (LS-SVM). In this paper, we present a simple, but efficient, greedy algorithm for constructing near optimal sparse approximations of least-squares support vector machines, in which at each iteration the training pattern minimising the regularised empirical risk is introduced into the kernel expansion. The proposed method demonstrates superior performance when compared with the pruning technique described by Suykens et al. [1], over the motorcycle and Boston housing datasets.",

author = "Cawley, {Gavin C.} and Talbot, {Nicola L. C.}",

year = "2002",

doi = "10.1007/3-540-46084-5_111",

language = "English",

isbn = "978-3-540-44074-1",

volume = "2415",

series = "Lecture Notes in Computer Science",

publisher = "Springer Berlin / Heidelberg",

pages = "681--686",

editor = "Dorronsoro, {Jos{\'e} R.}",

booktitle = "Artificial Neural Networks — ICANN 2002",

note = "Proceedings of the International Conference on Artificial Neural Networks ; Conference date: 28-08-2002 Through 30-08-2002",

}