Improving the accuracy and speed of support vector learning machines

By C. Burges, Bernhard Schölkopf, Mozer, M. Jordan, T. Petsche. 1997

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Citations in the corpus

Toward Causal Representation Learning (2021, methodology): “Instead of computing the maximum over all distributions in E, one can relax the problem by sampling from the interventional distributions and optimize an expectation over the different augmented images on a suitably chosen subset [39], using a search algorithm, such as RL [49] or an algorithm based ”
Towards Causal Representation Learning (2021, methodology): “Instead of computing the maximum over all distributions in E, one can relax the problem by sampling from the interventional distributions and optimize an expectation over the different augmented images on a suitably chosen subset [38], using a search algorithm like reinforcement learning [48] or an”
Uniqueness of the SVM Solution (1999, methodology): “6This method was used to estimate b under similar circumstances in [8].”
A Tutorial on Support Vector Machines for Pattern Recognition (1998, methodology): “…used for isolated handwritten digit recognition (Cortes and Vapnik, 1995; Schölkopf, Burges and Vapnik, 1995; Schölkopf, Burges and Vapnik, 1996; Burges and Schölkopf, 1997), object recognition (Blanz et al., 1996), speaker identification (Schmidt, 1996), charmed quark detection 1 , face…”
Hash Kernels (2009, methodology): “We denote by X the domain of observations and we assume that H is a Reproducing Kernel Hilbert Space H with kernel k : X× X→ R. Keeping the kernel expansion small One line of research [Burges and Schölkopf, 1997] aims to reduce the number of basis functions needed in the overall function expansion.”
Hash Kernels for Structured Data (2009, unknown): “Keeping the kernel expansion small One line of research Burges and Schölkopf (1997) aims to reduce the number of basis functions needed in the overall function expansion. This led to a number of reduced set Support Vector algorithms which work as follows: a) solve the full estimation problem resulti”
Advances in Large Margin Classifiers (2000, background): “Seen in this light, it is not too surprising that it is possible to give a mechanical interpretation of optimal margin hyperplanes (Burges and Schölkopf, 1997): If we assume that each support vector xi exerts a perpendicular force of size αi and sign yi on a solid plane sheet lying along the…”
A tutorial on support vector regression (2004, unknown): “Note, however, that also a reduced set algorithm like in [Burges, 1996, Burges and Sch¤olkopf, 1997, Sch¤olkopf et al., 1999b] or sparse decomposition techniques [Smola and Sch¤olkopf, 2000] could be applied to address this issue.”
A tutorial on?-support vector machines (2005, background): “Seen in this light, it is not too surprising that it is possible to give a mechanical interpretation of optimal margin hyperplanes ([9]): If we assume that each support vector xi exerts a perpendicular force of size αi and sign yi on a solid plane sheet lying along the hyperplane, then the solution ”