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- Robert Burbidge, Matthew W. B. Trotter, Bernard F. Buxton, Sean B. Holden
- Computers & Chemistry
- 2001

We show that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure-activity relationship analysis. In a benchmark test, the SVM is compared to several machine learning techniques currently used in the field. The classification task involves predicting the… (More)

- Andrew Naish, Sean B. Holden
- NIPS
- 2007

We present an efficient generalization of the sparse pseudo-input Gaussian process (SPGP) model developed by Snelson and Ghahramani [1], applying it to binary classification problems. By taking advantage of the SPGP prior covariance structure, we derive a numerically stable algorithm with O(NM) training complexity—asymptotically the same as related sparse… (More)

- Peter Hammond, Tim J Hutton, +9 authors Robin M Winter
- American journal of medical genetics. Part A
- 2004

Dense surface models can be used to analyze 3D facial morphology by establishing a correspondence of thousands of points across each 3D face image. The models provide dramatic visualizations of 3D face-shape variation with potential for training physicians to recognize the key components of particular syndromes. We demonstrate their use to visualize and… (More)

- Martin Anthony, Sean B. Holden
- Complex Systems
- 1994

Abst ract . Th e Vapn ik-Chervonenkis dimension has proven to be of great use in the theoret ical study of generalizat ion in artificial neural networks. Th e "probably approximately correct" learning framework is described and the importance of the Vapnik-Chervonenkis dimension is illustrated. We then investigate the Vapnik-Chervonenkis dimension of… (More)

- Martin Anthony, Sean B. Holden
- COLT
- 1998

This paper concerns the use of real-valued functions for binary classification problems. Previous work in this area has concentrated on using as an error estimate the ‘resubstitution’ error (that is, the empirical error of a classifier on the training sample) or its derivatives. However, in practice, cross-validation and related techniques are more popular.… (More)

- Martin Anthony, Sean B. Holden
- COLT
- 1993

In this paper we examine the representational and expressive power of two types of linearly weighted neural network: the polynomial discriminators (PDFs) and the radial basis function networks (RBFNs). A {O, 1}-valued function on Rn is a polyne mial discriminator of degree at most k if there is a surface in Rn which separates the positive examples of ! from… (More)

- Jeevani Wickramaratna, Sean B. Holden, Bernard F. Buxton
- Multiple Classifier Systems
- 2001

- Sean B. Holden, Peter J. W. Rayner
- IEEE Trans. Neural Networks
- 1995

The ability of connectionist networks to generalize is often cited as one of their most important properties. We analyze the generalization ability of the class of generalized single-layer networks (GSLNs), which includes Volterra networks, radial basis function networks, regularization networks, and the modified Kanerva model, using techniques based on the… (More)

- Sean B. Holden, Mahesan Niranjan
- Neural Computation
- 1995

This article addresses the question of whether some recent Vapnik-Chervonenkis (VC) dimension-based bounds on sample complexity can be regarded as a practical design tool. Specifically, we are interested in bounds on the sample complexity for the problem of training a pattern classifier such that we can expect it to perform valid generalization. Early… (More)

We applied two state-of-the-art machine learning techniques to the problem of selecting a good heuristic in a first-order theorem prover. Our aim was to demonstrate that sufficient information is available from simple feature measurements of a conjecture and axioms to determine a good choice of heuristic, and that the choice process can be automatically… (More)