J. Kivinen, A. J. Smola, and R. C. Williamson. Online learning with kernels. Signal Processing, IEEE Transactions on, 52(8):2165--2176, 2004. Home/Search Document Details and Download
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Classifying Large Data Sets Using SVM with Hierarchical Clusters - Yu, Yang, Han (2003) (1 citation) (Correct)
....while it is faster to be solved by a QP solver [8, 1] However, their new formulations are still not proven to be efficient and reliable enough to work with very large data sets. On line SVMs or incremental and decremental SVMs have been developed to handle dynamically incoming data efficiently [21, 5, 16]. In this senario that an SVMmodel is incrementally constructed and maintained, the newer data have a higher impact on the SVM model than older data. In other words, recent data have a higher chance to be the SVs of the SVM model than older data. Thus, for the analysis of an archive data which ....
J. Kivinen, A. J. Smola, and R. C. Williamson. Online learning with kernels. In Proc. Advances in Neural Information Processing Systems, Cambridge, MA, 2002.
Online Learning with Kernels - Kivinen, Smola, Williamson (2002) (5 citations) Self-citation (Kivinen Smola Williamson) (Correct)
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J. Kivinen, A. J. Smola, and R. C. Williamson, "Online learning with kernels," in Advances in Neural Information Processing Systems 14, T. G. Dietterich, S. Becker, and Z. Ghahramani, Eds. Cambridge, MA: MIT Press, 2002, pp. 785--792.
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J. Kivinen, A. J.S0xx and R. C. Williamson. Online learning with kernels. In T. G. Dietterich, S Becker and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 785--792. MIT Press, Cambridge, MA, 2002.
Online Bayes Point Machines - Harrington, Herbrich, Kivinen.. Self-citation (Kivinen Williamson) (Correct)
....algorithm which explicitly maximises the margin. 1 Introduction An online classi er tries to give the best prediction based on the example sequence seen at time t in contrast to a batch classi er which waits for the whole sequence of T examples. There have been a number of recent attempts [1 3] in the online setting to achieve a approximation to the maximum margin solution of batch learners such as SVMs. In this paper we present a truly online algorithm for linear classi ers which achieves a large margin by estimating the centre of mass (the so called Bayes point) by a randomisation ....
....iterating over the sample z. Second, even in a truly online setting, a large margin solution provides some immunity to attribute noise and concept drift. There have been a few recent attempts to develop further online algorithms that achieve an approximation to the maximum margin. Kivinen et al. [3] studied the marginalised perceptron (and issues arising when it is kernelised) Li and Long [2] studied an algorithm they called ROMMA where if there is a mistake at the tth trial then w t 1 is the smallest element of the constrained set of w : w t w kw t k fw : y t (w x t 1)g, ....
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J. Kivinen, A. Smola, and R. C. Williamson, (2002) Online Learning with kernels. Advances in Neural Information Processing Systems 14, Cambridge, MA: MIT Press (pp. 785-793).
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J. Kivinen, A. J. Smola, and R. C. Williamson. Online learning with kernels. Signal Processing, IEEE Transactions on, 52(8):2165--2176, 2004.
An Online Kernel Change Detection Algorithm - Desobry, Davy (2004) (Correct)
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J. Kivinen, A. Smola, and R. Williamson, Online learning with kernels, 2003, forthcoming.
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J. Kivinen, A. J. Smola, and R. C. Williamson. Online learning with kernels. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.
On-Line One-Class Support Vector Machines. An Application to .. - Gretton, Desobry (2003) (Correct)
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J. Kivinen, A. J. Smola, and R. C. Williamson, "Online learning with kernels," in Advances in Neural Information Processing Systems 14, T. G. Dietterich, S. Becker, and Z. Ghahramani, Eds., Cambridge, MA, 2002, MIT Press.
Online Passive-Aggressive Algorithms - Crammer, Dekel, Shalev-Shwartz.. (2003) (Correct)
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J. Kivinen, A. J. Smola, and R. C. Williamson. Online learning with kernels. In NIPS'02.
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