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Original Articles

Semi-Supervised Logistic Discrimination via Regularized Gaussian Basis Expansions

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Pages 2412-2423 | Received 17 Jun 2009, Accepted 25 Mar 2010, Published online: 13 Apr 2011

References

  • Amini , M.-R. , Gallinari , P. ( 2002 ). Semi-supervised logistic regression. Proc. 15th Euro. Conf. Artif. Intell. 390–394 .
  • Ando , T. , Konishi , S. ( 2009 ). Nonlinear logistic discrimination via regularized radial basis functions for classifying high-dimensional data . Ann. Instit. Statist. Math. 61 : 331 – 353 .
  • Bennett , K. P. , Demiriz , A. ( 1998 ). Semi-supervised support vector machines . Adv. Neur. Inform. Process. Syst. 11 : 368 – 374 .
  • Bennett , K. P. , Demiriz , A. , Maclin , R. ( 2002 ). Exploiting unlabeled data in ensemble methods. Proc. ACM Int. Conf. Knowledge Discov. Data Mining, pp. 289–296 .
  • Bishop , C. M. ( 2006 ). Pattern Recognition and Machine Learning . New York : Springer .
  • Chapelle , O. , Schölkopf , B. , Zien , A. ( 2006 ). Semi-Supervised Learning . Cambridge , MA : MIT Press .
  • Chen , K. , Wang , S. ( 2007 ). Regularized boost for semi-supervised learning . Adv. Neur. Inform. Process. Syst. 20 : 281 – 288 .
  • Dean , N. , Murphy , T. B. , Downey , G. ( 2006 ). Using unlabelled data to update classification rules with applications in food authenticity studies . J. Roy. Statist. Soc. C 55 : 1 – 14 .
  • Dempster , A. P. , Laird , N. M. , Rubin , D. B. ( 1977 ). Maximum likelihood from incomplete data via the EM algorithm (with discussion) . J. Roy. Statist. Soc. B 39 : 1 – 38 .
  • Green , P. J. , Silverman , B. W. ( 1994 ). Nonparametric Regression and Generalized Linear Models . London : Chapman & Hall .
  • Hastie , T. , Tibshirani , R. , Friedman , J. ( 2009 ). The Elements of Statistical Learning. , 2nd ed. New York : Springer .
  • Kai , Y. , Tresp , V. , Zhou , D. ( 2004 ). Semi-supervised induction with basis functions. Technical Report. Department of Empirical Inference, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany .
  • Konishi , S. , Ando , T. , Imoto , S. ( 2004 ). Bayesian information criteria and smoothing parameter selection in radial basis function networks . Biometrika 91 : 27 – 43 .
  • Konishi , S. , Kitagawa , G. ( 2008 ). Information Criteria and Statistical Modeling . New York : Springer .
  • LeCun , Y. , Bottou , L. , Bengio , Y. , Haffner , P. ( 1998 ). Gradient-based learning applied to document recognition . Proc. IEEE 86 : 2278 – 2324 .
  • Liang , F. , Mukherjee , S. , West , M. ( 2007 ). The use of unlabeled data in predictive modeling . Statist. Sci. 22 : 189 – 205 .
  • Miller , D. , Uyar , H. S. ( 1997 ). A mixture of experts classifier with learning based on both labelled and unlabelled data . Adv. Neur. Inform. Proces. Syst. 9 : 571 – 577 .
  • Moody , J. , Darken , C. J. ( 1989 ). Fast learning in networks of locally-tuned processing units . Neur. Computat. 1 : 281 – 294 .
  • Schwarz , G. ( 1978 ). Estimating the dimension of a model . Ann. Statist. 6 : 461 – 464 .
  • Sigillito , V. G. , Wing , S. P. , Hutton , L. V. , Baker , K. B. ( 1989 ). Classification of radar returns from the ionosphere using neural networks . Johns Hopkins APL Techn. Dig. 10 : 262 – 266 .
  • Tierney , L. , Kadane , J. B. (1986). Accurate approximations for posterior moments and marginal densities. J. Amer. Statist. Assoc. 81:82–86.
  • Vapnik , V. ( 1998 ). Statistical Learning Theory . New York : Wiley .
  • Vittaut , J.-N. , Amini , M.-R. , Gallinari , P. ( 2002 ). Learning classification with both labeled and unlabeled data. Proc. 13th Eur. Conf. Mach. Learn. 468–479 .
  • Zhou , D. , Bousquet , O. , Lal , T. N. , Weston , J. , Schölkopf , B. ( 2004 ). Learning with local and global consistency . Adv. Neur. Inform. Process. Syst. 16 : 321 – 328 .

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