Glasmachers T, Igel C. (2008). Second-order SMO improves SVM online and active learning. Neural computation. 20 [PubMed]

See more from authors: Glasmachers T · Igel C

References and models cited by this paper

Bottou L, Weston J, Bordes A, Ertekin S. (2005). Fast kernel classifiers with on-line and active learning J Mach Learn Res. 5

Chapelle O, Decoste D, Keerthi S. (2006). Building support vector machines with reduced classifier complexity J Mach Learn Res. 8

Glasmachers T, Igel C. (2005). Gradient-based adaptation of general gaussian kernels. Neural computation. 17 [PubMed]

Glasmachers T, Igel C. (2006). Maximum-gain working set selection for support vector machines J Mach Learn Res. 7

Joachims T. (1999). Making large-scale SVM learning practical Advances in kernel methods-Support vector learning.

Kwok JT, Tsang IW, Cheung PM. (2005). Core vector machines: Fast SVM training on very large data sets J Mach Learn Res. 6

Lin CJ, Fan RE, Chen PH. (2005). Working set selection using second order information for training support vector machines J Mach Learn Res. 6

Platt J. (1999). Fast training of support vector machines using sequential minimal optimization Advances in kernel methods: Support vector learning.

Smola AJ, Murty MN, Vishwanathan SVN. (2003). Simple SVM Proc 20th Intl Conf Mach Learn.

Vapnik V, Cortes C. (1995). Support-vector networks Mach Learn. 20

References and models that cite this paper
This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.