[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.
[On
line].
[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.