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NIPS
1996
9 years 9 months ago
Improving the Accuracy and Speed of Support Vector Machines
Support Vector Learning Machines (SVM) are nding application in pattern recognition, regression estimation, and operator inversion for ill-posed problems. Against this very genera...
Christopher J. C. Burges, Bernhard Schölkopf
NIPS
2000
9 years 9 months ago
From Margin to Sparsity
We present an improvement of Noviko 's perceptron convergence theorem. Reinterpreting this mistakebound as a margindependent sparsity guarantee allows us to give a PAC{style ...
Thore Graepel, Ralf Herbrich, Robert C. Williamson
NIPS
2004
9 years 9 months ago
Sub-Microwatt Analog VLSI Support Vector Machine for Pattern Classification and Sequence Estimation
An analog system-on-chip for kernel-based pattern classification and sequence estimation is presented. State transition probabilities conditioned on input data are generated by an...
Shantanu Chakrabartty, Gert Cauwenberghs
DICTA
2003
9 years 9 months ago
Algebraic Curve Fitting Support Vector Machines
An algebraic curve is defined as the zero set of a multivariate polynomial. We consider the problem of fitting an algebraic curve to a set of vectors given an additional set of v...
Christian J. Walder, Brian C. Lovell, Peter J. Koo...
IMAGING
2004
9 years 9 months ago
Estimating Illumination Chromaticity via Support Vector Regression
The technique of support vector regression is applied to the problem of estimating the chromaticity of the light illuminating a scene from a color histogram of an image of the sce...
Brian V. Funt, Weihua Xiong
ESANN
2006
9 years 9 months ago
OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method
Abstract. We present the OnlineDoubleMaxMinOver approach to obtain the Support Vectors in two class classification problems. With its linear time complexity and linear convergence ...
Daniel Schneegaß, Thomas Martinetz, Michael ...
DAGM
2008
Springer
9 years 9 months ago
Simple Incremental One-Class Support Vector Classification
We introduce the OneClassMaxMinOver (OMMO) algorithm for the problem of one-class support vector classification. The algorithm is extremely simple and therefore a convenient choice...
Kai Labusch, Fabian Timm, Thomas Martinetz
ECML
2006
Springer
9 years 10 months ago
Efficient Large Scale Linear Programming Support Vector Machines
This paper presents a decomposition method for efficiently constructing 1-norm Support Vector Machines (SVMs). The decomposition algorithm introduced in this paper possesses many d...
Suvrit Sra
ANNPR
2006
Springer
9 years 11 months ago
Incremental Training of Support Vector Machines Using Truncated Hypercones
We discuss incremental training of support vector machines in which we approximate the regions, where support vector candidates exist, by truncated hypercones. We generate the trun...
Shinya Katagiri, Shigeo Abe
GECCO
2007
Springer
180views Optimization» more  GECCO 2007»
9 years 11 months ago
Support vector regression for classifier prediction
In this paper we introduce XCSF with support vector prediction: the problem of learning the prediction function is solved as a support vector regression problem and each classifie...
Daniele Loiacono, Andrea Marelli, Pier Luca Lanzi
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