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{"Anderson62" =>"T.W.Anderson and R.R.Bahadur. Classification into two multivariate normal distributions with differrentia covariance matrices. Anals of Mathematical Statistics, 33:420--431, June 1962.","Baudat01" =>"G.Baudat and F.Anouar. Generalized discriminant analysis using a kernel approach. Neural Computation, 12(10):2385--2404, 2000.","Bishop97" =>"C.M.Bishop. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, Great Britain, 3th edition, 1997.","Cris00" =>"N.Cristianini and J.Shawe-Taylor. Support Vector Machines. Cambridge University Press, 2000.","DLR77" =>"A.P.Dempster, N.M.Laird, and D.B.Rubin. Maximum likelihood from incomplete data via the {EM} {A}lgorithm. Journal of the Royal Statistical Society, 39:185--197, 1977.","DHS01" =>"R.O.Duda, P.E.Hart, and D.G.Stork. Pattern Classification. John Wiley \& Sons, 2nd. edition, 2001.","Duin00" =>"R.P.W.Duin. Prtools: A matlab toolbox for pattern recognition, 2000.","Franc2000" =>"V.Franc. Programov{\'e} n{\'a}stroje pro rozpozn{\'a}v{\'a}n{\'\i} ({P}attern {R}ecognition {P}rogramming {T}ools, {I}n {C}zech). Master's thesis, {\v C}esk{\' e} vysok{\' e} u{\v c}en{\'\i} technick{\' e}, Fakulta elektrotechnick{\'a}, Katedra kybernetiky, February 2000.","Franc02" =>"V.Franc and V.Hlav{\'a}{\v c}. Multi-class support vector machine. In R.Kasturi, D.Laurendeau, and SuenC., editors, 16th International Conference on Pattern Recognition, vol. 2, pages 236--239. IEEE Computer Society, 2002.","Franc03" =>"V.Franc and V.Hlav{\'a}{\v c}. An iterative algorithm learning the maximal margin classifier. Pattern Recognition, 36(9):1985--1996, 2003.","Franc03b" =>"V.Franc and V.Hlav\'a\v{c}. Greedy algorithm for a training set reduction in the kernel methods. In N.Petkov and M.A.Westenberg, editors, Computer Analysis of Images and Patterns, pages 426--433, Berlin, Germany, 2003. Springer.","Girol03" =>"M.Girolami and C.He. Probability density estimation from optimally condensed data samples. IEEE Transactions on Pattern Analysis and Machine Learning, 25(10):1253--1264, October 2003.","Hsu02" =>"C.W.Hsu and C.J.Lin. A comparison of methods for multiclass support vector machins. IEEE Transactions on Neural Networks, 13(2), March 2002.","Jollife86" =>"I.T.Jollife. Principal Component Analysis. Springer-Verlag, New York, 1986.","Keerthi00" =>"S.S.Keerthi, S.K.Shevade, C.Bhattacharya, and K.R.K.Murthy. A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Transactions on Neural Networks, 11(1):124--136, January 2000.","Kim04" =>"KimKwang, In, FranzMatthias, O., and Sch\"olkopfBernhard. Kernel hebbian algorithm for single-fram super-resolution. In LeonardisAle\v{s} and BischofHorst, editors, Statisical Learning in Computer Vision, ECCV Workshop. Springer, May 2004.","Kwok03" =>"J.T.Kwok and I.W.Tsang. The pre-image problem in kernel methods. In Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), pages 408--415, Washington, D.C., USA, August 2003.","LeCun89" =>"Y.LeCun, B.Boser, J.S.Denker, D.Henderson, R.E.Howard, W.Hubbard, and L.JJackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1:541--551, 1989.","McLachlan97" =>"G.McLachlan and T.Krishnan. The EM Algorithm and Extensions. John Wiley \& Sons, New York, 1997.","Mika99a" =>"S.Mika, G.R\"atsch, J.Weston, B.Sch\"olkpf, and K.M\"uller. Fisher discriminant analysis with kernel. In Y.H.Hu, J.Larsen, and S.Wilson, E.~Douglas, editors, Neural Networks for Signal Processing, pages 41--48. IEEE, 1999.","Mika99b" =>"S.Mika, B.Sch\"olkopf, A.Smola, K.R.M\"uller, M.Scholz, and G.R\"atsch. Kernel pca and de-noising in feature spaces. In M.S.Kearns, S.A.Solla, and D.A.Cohn, editors, Advances in Neural Information Processing Systems 11, pages 536 -- 542, Cambridge, MA, 1999. MIT Press.","Nabney02" =>"I.T.Nabney. NETLAB~: algorithms for pattern recognition. Advances in pattern recognition. Springer, London, 2002.","Platt99a" =>"J.Platt. Probabilities for sv machines. In A.J.Smola, P.J.Bartlett, B.Scholkopf, and D.Schuurmans, editors, Advances in Large Margin Classifiers (Neural Information Processing Series). MIT Press, 2000.","Platt98" =>"J.C.Platt. Sequential minimal optimizer: A fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, Microsoft Research, Redmond, 1998. http://www.research.microsoft.com/~jplatt/smo.html.","Riply94" =>"B.D.Riply. Neural networks and related methods for classification (with discusion). J. Royal Statistical Soc. Series B, 56:409--456, 1994.","Schles68" =>"M.I.Schlesinger. {A} connection between learning and self-learning in the pattern recognition (in {R}ussian). Kibernetika, 2:81--88, 1968.","SH10" =>"M.I.Schlesinger and V.Hlav{\'a}{\v c}. Ten lectures on statistical and structural pattern recognition. Kluwer Academic Publishers, 2002.","Schol99" =>"B.Sch\"olkopf, C.Burges, and A.J.Smola. Advances in Kernel Methods -- Support Vector Learning. MIT Press, Cambridge, 1999.","Schol98a" =>"B.Sch{\"o}lkopf, P.Knirsch, and C.Smola, A.~Burges. Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature spaces. In P.Levi, M.Schanz, R.J.Ahler, and F.May, editors, Mustererkennung 1998-20. DAGM., pages 124--132, Berlin, Germany, 1998. Springer-Verlag.","Schol98b" =>"B.Scholkopf, A.Smola, and K.R.Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299--1319, 1998.","Schol02" =>"B.Sch{\"o}lkopf and A.J.Smola. Learning with Kernels. The MIT Press, MA, 2002.","Vapnik95" =>"V.Vapnik. The nature of statistical learning theory. Springer Verlag, 1995.",};
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