Simon I. Hill

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(EGU¦ algorithm). These algorithms have been previously analyzed using the " mistake-bound framework " in the computational learning theory community. In this paper we perform a traditional signal processing analysis in terms of the mean square error. A relationship between the learning rate and the mean squared error (MSE) of predictions is found for the(More)
In this paper, the perceptually based loss functions for audio filtering used by Wolfe and Godsill [1] are shown to fit well within a complex-valued Support Vector Machine (SVM) framework. SVM regression is extended to estimation of complex-valued functions, including the derivation of a variant of the Sequential Minimal Op-timisation (SMO) algorithm. Audio(More)
A geometric construction is presented which is shown to be an effective tool for understanding and implementing multi-category support vector classification. It is demonstrated how this construction can be used to extend many other existing two-class kernel-based classification methodologies in a straightforward way while still preserving attractive(More)
A geometric framework for understanding multi-category classification is introduced, through which many existing 'all-together' algorithms can be understood. The structure enables parsimonious optimisation, through a direct extension of the binary methodology. The focus is on Support Vector Classification, with parallels drawn to related methods. The(More)
The focus of this work is on the problem of tracking parameters describing both the stochastic discount factor and the objective / real-world measure dynamically, with the aim of monitoring value at risk or other related diagnostics of interest. The methodology presented incorporates information from derivative prices as well as from the underlying(More)