Robert C. Williamson

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Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f(More)
We describe a new class of Support Vector algorithms for regression and classi cation In these algorithms a parameter lets one e ectively con trol the number of Support Vectors While this can be useful in its own right the parametrization has the additional bene t of enabling us to eliminate one of the other free parameters of the algorithm the accuracy(More)
Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little use of these methods in an online setting suitable for real-time(More)
Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified between and . We propose a method to approach this problem by trying to estimate a function f which is(More)
The paper introduces some generalizations of Vapnik’s method of structural risk minimisation (SRM). As well as making explicit some of the details on SRM, it provides a result that allows one to trade off errors on the training sample against improved generalization performance. It then considers the more general case when the hierarchy of classes is chosen(More)
Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed(More)
Probabilistic arithmetic involves the calculation of the distribution of arithmetic functions of random variables. This work on probabilistic arithmetic began as an investigation into the possibility o f adapting existing numerical procedures (developal for fixed numbers) to handle random variables (by replacing the basic operations of arithmetic by the(More)
This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical estimation problem very much akin to the problem of minimizing a(More)
A new algorithm for Support Vector regression is described. For a priori chosen , it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction of the data points lie outside. Moreover, it is shown how to use parametric tube shapes with non-constant radius. The algorithm is analysed theoretically and experimentally.
This paper investigates the robustness of sound equalization using a room response inverse lter with respect to changing or uncertain source or microphone positions It is shown that due to the variations of the transfer function from point to point in a room even small changes in the source or microphone position of just a few tenths of the acoustic(More)