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- David J. Crisp, Christopher J. C. Burges
- NIPS
- 1999

Christopher J.C. Burges Advanced Technologies, Bell Laboratories, Lucent Technologies Holmdel, New Jersey burges@lucent.com We show that the recently proposed variant of the Support Vector machine (SVM) algorithm, known as v-SVM, can be interpreted as a maximal separation between subsets of the convex hulls of the data, which we call soft convex hulls. The… (More)

- Christopher J. C. Burges, David J. Crisp
- NIPS
- 1999

We give necessary and sufficient conditions for uniqueness of the support vector solution for the problems of pattern recognition and regression estimation, for a general class of cost functions. We show that if the solution is not unique, all support vectors are necessarily at bound, and we give some simple examples of non-unique solutions. We note that… (More)

We show that the recently proposed variant of the Support Vector machine (SVM) algorithm, known as-SVM, can be interpreted as a maximal separation between subsets of the convex hulls of the data, which we call soft convex hulls. The soft convex hulls are controlled by choice of the parameter. If the intersection of the convex hulls is empty, the hyperplane… (More)

One of the more successful approaches to image segmentation involves formulating the problem as the minimisation of a Mumford-Shah functional and then using region merging algorithms to approximate the minimiser. Recent work by Redding et al. presented such an algorithm and demonstrated the quality of the segmentations it produces. Here, we extend that work… (More)

- Nick J. S. Stacy, David J. Crisp, Alvin S. Goh, Daniel Badger, Mark Preiss
- IGARSS
- 2005

- Christopher J. C. Burges, David J. Crisp
- Neurocomputing
- 2003

This paper has three goals: to find uniqueness theorems for some well-known kernel methods, and thereby to better understand their behavior; to derive theorems which apply to more general families of kernel methods; and to collect results which are hoped to be useful to workers who would like to prove uniqueness theorems for their own algorithms. Knowing… (More)

- D J CRISP, C P SPENCER
- Proceedings of the Royal Society of London…
- 1958

- David J. Crisp, Nick J. S. Stacy, D. A. Hudson, Paul B. Pincus, Alvin S. Goh
- IGARSS
- 2007

An estimated probability distribution of the backscatter is commonly used to determine the threshold for distinguishing targets from clutter at a given false alarm rate. Data collected at high grazing angles (15◦−45◦) by the Defence Science Technology Organisation’s Ingara fully polarimetric X-band radar demonstrates that the commonly used K-distribution is… (More)

- David J. Crisp, Peter G. Perry, Nicholas J. Redding
- ACSC
- 2003