The support vector machine under test
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Tài liệu tham khảo
Anguita, 2000, Evaluating the generalization ability of support vector machines through the bootstrap, Neural Process. Lett., 11, 51, 10.1023/A:1009636300083
P. Auer, H. Burgsteiner, W. Maass, Reducing communication for distributed learning in neural networks, in: Artificial Neural Networks—ICANN 2001, Springer, Berlin, 2002.
Ben-Hur, 2001, Support vector clustering, J. Mach. Learn. Res., 2, 125
C. Blake, C. Merz, UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences 1998, http://www.ics.uci.edu/~mlearn/MLRepository.html.
Breiman, 1998, Arcing classifiers, Ann. Stat., 26, 801
Breiman, 1984
Chambers, 1998
C.-C. Chang, C.-J. Lin, Libsvm: a library for support vector machines, 2001, available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Demirez, 2000, Optimization approaches to semisupervised learning
Friedman, 2002, Stochastic gradient boosting, Comput. Stat. Data Anal., 38, 367, 10.1016/S0167-9473(01)00065-2
T.V. Gestel, J. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B.D. Moor, J. Vandewalle, Benchmarking least squares support vector machine classifiers, Mach. Learn. 2004, to appear.
Y. Guermeur, A. Eliseeff, H. PaugamMoisy, A new multi-class svm based on a uniform convergence result, in: S.-I. Amari, C. Giles, M. Gori, V. Piuri (Eds.), Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks IJCNN 2000, Los Alamitos, IEEE Computer Society, 2000, pp. IV-183–IV-188.
Hastie, 2001
R. Herbrich, T. Graepel, C. Campbell, Bayes point machines: estimating the bayes point in kernel space, Proceedings of IJCAI Workshop Support Vector Machines, 1999, pp. 23–27.
Hothorn, 2003, Double-bagging: combining classifiers by bootstrap aggregation, Pattern Recognition, 36, 1303, 10.1016/S0031-3203(02)00169-3
Hsu, 2002, A comparison of methods for multi-class support vector machines, IEEE Trans. Neural Networks, 13, 415, 10.1109/72.991427
Kohavi, 1996, Bias plus variance decomposition for zero-one loss, 275
E.B. Kong, T.G. Dietterich, Error-correcting output coding corrects bias and variance, in: S. Prieditis, S. Russell (Eds.), Machine Learning: Proceedings of the 12th International Conference, Morgan-Kaufmann, Los Altos, CA, 1995, pp. 313–321.
F. Leisch, Ensemble Methods for Neural Clustering and Classification, Ph.D. Thesis, Technische Universität Wien, 1998, available from: http://www.ci.tuwien.ac.at/~leisch/papers/Leisch:1998.ps.gz.
F. Leisch, E. Dimitriadou, mlbench—a collection for artificial and real-world machine learning benchmarking problems. R package, Version 0.5-6, 2001, available from http://cran.R-project.org.
Lim, 2000, A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms, Machine Learn., 40, 203, 10.1023/A:1007608224229
Mangasarian, 2000, Robust linear and support vector regression, IEEE Trans. Pattern Anal. Mach. Intell., 22, 950, 10.1109/34.877518
Mayoraz, 2001, Multiclass classification with pairwise coupled neural networks or support vector machines
D. Meyer, Support vector machines, R News 1(3) (2001) 23–26. http://CRAN.R-project.org/doc/Rnews/.
Michie, 1994
Neal, 1998, Assessing relevance determination methods using delve generalization in neural networks and machine learning
L. Prechelt, Proben1—a set of neural network benchmark problems and benchmarking rules, Fakultaet fuer Informatik, Universitaet Karlsruhe, Germany, 1994, available from ftp://ftp.ira.uka.de/pub/papers/techreports/1994/1994-21.ps.gz.
Prechelt, 1995, Some notes on neural learning algorithm benchmarking, Neurocomputing, 9, 343, 10.1016/0925-2312(95)00084-1
Ralaivola, 2001, Incremental support vector machine learning: a local approach
Ripley, 1996
Tipping, 2000, The relevance vector machine
Vapnik, 1998
W. Venables, B.D. Ripley, Modern Applied Statistics with S-Plus, 3rd Edition, Springer, 1996, available at http://www.stats.ox.ac.uk/pub/MASS3/.
P. Vincent, Y. Bengio, A neural support vector network architecture with adaptive kernels, Proceedings of the Neural Information Processing Systems (NIPS) Conference, 2001.
J. Weston, C. Watkins, Multi-class support vector machines, Technical Report CSD-TR-98-04, Department of Computer Science, Royal Holloway, University of London, Egham, TW20 0EX, UK, 1998.