http://eprints.soton.ac.uk/id/eprint/260372 2024-03-14T06:38:08Z info:eu-repo/semantics/descriptiveMetadata https://eprints.soton.ac.uk/260372/ Estimating the moments of a random vector with applications Shawe-Taylor, John Cristianini, Nello A general result about the quality of approximation of the mean of a distribution by its empirical estimate is proven that does not involve the dimension of the feature space. Using the kernel trick this gives also bounds the quality of approximation of higher order moments. A number of applications are derived of interest in learning theory including a new novelty detection algorithm and rigorous bounds on the Robust Minimax Classification algorithm. 2003 Conference or Workshop Item PeerReviewed text en https://eprints.soton.ac.uk/260372/1/EstimatingTheMomentsOfARandomVectorWithApplications.pdf Shawe-Taylor, John and Cristianini, Nello (2003) Estimating the moments of a random vector with applications. In Proceedings of GRETSI 2003 Conference. pp. 47-52 . info:eu-repo/semantics/objectFile info:eu-repo/semantics/humanStartPage