Biomedical Engineering Reference
In-Depth Information
[47] M. Muller, ''A scaled conjugate gradient algorithm for fast supervised learning,'' Neural
Networks , vol. 6, pp. 525--533, 1993.
[48]
S. Fahlman, ''Faster-learning variations on back-propagation: An empirical study,'' in
Connectionist Models Summer School (T. Sejnowski, G. Hinton, and D. Touretzky, eds.),
(San Mateo, CA), Morgan Kaufmann, 1988.
[49]
L. Bottou and Y. LeCun, ''Large-scale online learning,'' in Advances in Neural Informa-
tion Processing Systems , vol. 15, Cambridge, MA: MIT Press, 2004.
[50]
P. Sundararajan, and N. Saratchandran, Parallel Architectures for Artificial Neural Net-
works: Paradigms and Implementations , Wiley-IEEE Computer Society Press, 1998.
[51]
B. Boser, I. Guyon, and V. Vapnik, ''A training algorithm for optimal margin classifiers,''
Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory
(D. Haussler, ed.), (Pittsburgh, PA), pp. 144--152, ACM Press, 1992.
[52]
J. Platt, ''Fast training of support vector machines using sequential minimal optimiza-
tion,'' in Advances in Kernel Methods - Support Vector Learning (A. Smola, P. Bartlett,
B. Scholkopf, and D. Schuurmans, eds.), (Cambridge, MA), pp. 185--208, MIT Press,
1999.
[53]
S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice-Hall, 1999.
[54]
C. Cortes and V. Vapnik,
''Support vector networks,'' Machine Learning , vol. 20,
pp. 273--297, 1995.
[55] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, ''Gene selection for cancer classification
using support vector machines,'' Machine Learning , vol. 46, no. 1-3, pp. 389--422, 2002.
[56] J. Weston, A. Elisseeff, B. Scholkopf, and M. Tipping, ''Use of the zero-norm with linear
models and kernel methods,'' Journal of Machine Learning Research , vol. 3, pp. 1439--
1461, 2003.
[57] I. W. Tsang, J. T. Kwok, and P.-M. Cheung, ''Core vector machines: Fast SVM training
on very large data sets,'' Journal of Machine Learning Research , vol. 6, pp. 363--392,
2005.
[58] R. Collobert, S. Bengio, and Y. Bengio, ''A parallel mixture of SVMs for very large scale
problems,'' in Advances in Neural Information Processing Systems , MIT Press, 2002.
[59] G. Zanghirati and L. Zanni, ''A parallel solver for large quadratic programs in training
support vector machines,'' Parallel Computing , vol. 29, pp. 535--551, 2003.
[60] H. P. Graf, E. Cosatto, L. Bottou, I. Durdanovic, and V. Vapnik, ''Parallel support vector
machines: The cascade SVM,'' in Advances in Neural Information Processing Systems ,
2004.
[61] E. Yom-Tov, ''A distributed sequential solver for large-scale SVMs,'' in Large Scale Kernel
Machines (O. Chapelle, D. DeCoste, J. Weston, and L. Bottou, eds.), (Cambridge, MA),
pp. 141--156, MIT Press, 2007.
[62] A. Bordes and L. Bottou, ''The huller: a simple and efficient online SVM,'' in Machine
Learning: ECML 2005 , Lecture Notes in Artificial Intelligence, LNAI 3720, pp. 505--512,
Springer Verlag, 2005.
[63] O. Dekel, S. Shalev-Shwartz, and Y. Singer, ''The forgetron: A kernel-based perceptron
on a budget,'' SIAM J. Comput. , vol. 37, no. 5, pp. 1342--1372, 2008.
[64]
C. Yu and D. Skillicorn, ''Parallelizing boosting and bagging,'' Technical report, Queen's
University, Kingston, 2001.
[65]
Y. Freund and R. Schapire, ''A decision-theoretic generalization of online learning and an
application to boosting,'' Journal of Computer and System Sciences , vol. 55, pp. 119--139,
1995.
[66]
R. Meir, R. El-Yaniv, and S. Ben-David, ''Localized boosting,'' Proceedings of the 13th
Annual Conference on Computer Learning Theory , (San Francisco), pp. 190--199, Mor-
gan Kaufmann, 2000.
Search WWH ::




Custom Search