Next: About this document ...
Up: Bayesian Field Theory Nonparametric
Previous: Density estimation with Gaussian
Contents
-
- 1
-
Aarts, E. & Korts, J. (1989)
Simulated Annealing and Boltzmann Machines.
New York: Wiley.
- 2
-
Abu-Mostafa, Y. (1990)
Learning from Hints in Neural Networks.
Journal of Complexity
6, 192-198.
- 3
-
Abu-Mostafa, Y. (1993)
Hints and the VC Dimension.
Neural Computation
5, 278-288.
- 4
-
Abu-Mostafa, Y. (1993b)
A method for learning from hints.
Advances in Neural Information Processing Systems
5, S. Hanson et al (eds.), 73-80,
San Mateo, CA: Morgan Kauffmann.
- 5
-
Aida, T. (1999)
Field Theoretical Analysis of On-line Learning
of Probability Distributions.
Phys. Rev. Lett. 83, 3554-3557,
arXiv:cond-mat/9911474.
- 6
-
Allen, D.M. (1974)
The relationship between variable selection and data
augmentation and a method of prediction.
Technometrics 16, 125.
- 7
-
Amari, S., Cichocki, A., & Yang, H.H.(1996)
A New Learning Algorithm for Blind Signal Separation.
in Advances in Neural Information Processing Systems 8,
D.S. Touretzky et al (eds.), 757-763,
Cambridge, MA: MIT Press.
- 8
-
Ames, W.F. (1977)
Numerical Methods for Partial Differential Equations.
(2nd. ed.)
New York: Academic Press.
- 9
-
Balian, R. (1991)
From Microphysics to Macrophysics.
Vol. I.
Berlin: Springer Verlag.
- 10
-
Ballard, D.H. (1997)
An Introduction to Natural Computation.
Cambridge, MA: MIT Press.
- 11
-
Bayes, T.R. (1763)
An Essay Towards Solving a Problem in the Doctrine of Chances.
Phil. Trans. Roy. Soc. London 53, 370.
(Reprinted in Biometrika (1958) 45, 293)
- 12
-
Bazaraa, M.S., Sherali, H.D., & Shetty, C.M. (1993)
Nonlinear Programming. (2nd ed.)
New York: Wiley.
- 13
-
Beck, C. & Schlögl, F. (1993)
Thermodynamics of chaotic systems.
Cambridge: Cambridge University Press.
- 14
-
Bell, A.J. & Sejnowski, T.J. (1995)
Neural Computation 7(6), 1129-1159.
- 15
-
Ben-Israel, A. & Greville, T.N.E. (1974)
Generalized Inverses: Theory and Applications
New York: Wiley.
- 16
-
Berger, J.O. (1980)
Statistical Decision Theory and Bayesian Analysis.
New York: Springer Verlag.
- 17
-
Berger, J.O. & Wolpert R. (1988)
The Likelihood Principle.
(2nd ed.)
Hayward, CA:
IMS Lecture Notes -- Monograph Series 9.
- 18
-
Bernado, J.M. & Smith, A.F. (1994)
Bayesian Theory.
New York: John Wiley.
- 19
-
Bertsekas, D.P. (1995)
Nonlinear Programming.
Belmont, MA: Athena Scientific.
- 20
-
Bialek, W., Callan, C.G., & Strong, S.P. (1996)
Field Theories for Learning Probability Distributions.
Phys. Rev. Lett. 77, 4693-4697,
arXiv:cond-mat/9607180.
- 21
-
Binder, K. & Heermann, D.W. (1988)
Monte Carlo simulation in statistical physics: An introduction.
Berlin: Springer Verlag.
- 22
-
Bishop, C.M. (1993)
Curvature-driven smoothing:
A learning algorithm for feedforward networks.
IEEE Transactions on Neural Networks 4(5),882-884.
- 23
-
Bishop, C.M. (1995)
Training with noise is equivalent
to Tikhonov regularization.
Neural Computation
7 (1), 108-116.
- 24
-
Bishop, C.M. (1995)
Neural Networks for Pattern Recognition.
Oxford: Oxford University Press.
- 25
-
Bishop, E. & Bridges, D. (1985)
Constructive Analysis.
Grundlehren der Mathematischen Wissenschaften,
Vol. 279.
Berlin: Springer Verlag.
- 26
-
Black, M.J. & Rangarajan, A. (1996)
On the Unification of Line Processes, Outlier Rejection,
and Robust Statistics With Applications in Early Vision.
Int'l J. Computer Vision 19 (1).
- 27
-
Blaizot, J.-P. & Ripka, G. (1986)
Quantum Theory of Finite Systems.
Cambridge, MA: MIT Press.
- 28
-
Blake, A. & Zisserman, A. (1987)
Visual reconstruction
Cambridge, MA: MIT Press.
- 29
-
Blanchard, P. & Bruening, E. (1982)
Variational Methods in Mathematical Physics.
Berlin: Springer Verlag.
- 30
-
Bleistein, N. & Handelsman, N. (1986)
Asymptotic Expansions of Integrals.
(Originally published in 1975
by Holt, Rinehart and Winston, New York)
New York: Dover.
- 31
-
Breiman, L. (1993)
Hinging hyperplanes for regression, classification,
and function approximation.
IEEE Trans. Inform. Theory 39(3), 999-1013.
- 32
-
Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1993)
Classification and Regression Trees.,
New York: Chapman & Hall.
- 33
-
Bretthorst, G.L. (1988)
Bayesian spectrum analysis and parameter estimation.
Lecture Notes in Statistics, Vol. 48.
Berlin: Springer Verlag.
(Available at http://bayes.wustl.edu/glb/book.pdf)
- 34
-
Cardy, J. (1996)
Scaling and Renormalization in Statistical Physics.
Cambridge: Cambridge University Press.
- 35
-
Carlin, B.P. & Louis T.A. (1996)
Bayes and Empirical Bayes Methods for Data Analysis.
Boca Raton: Chapman & Hall/CRC.
- 36
-
Choquet-Bruhat Y., DeWitt-Morette, C., & Dillard-Bleick, M. (1982)
Analysis, Manifolds, and Physics. Part I.
Amsterdam: North-Holland.
- 37
-
Collins, J. (1984)
Renormalization.
Cambridge: Cambridge University Press.
- 38
-
Cox, D.R. & Hinkley, D.V. (1974)
Theoretical Statistics.
London: Chapman & Hall.
- 39
-
Craven, P. & Wahba, G. (1979)
Smoothing noisy data with spline functions:
estimating the correct degree of smoothing by the method
of generalized cross-validation.
Numer. Math. 31, 377-403.
- 40
-
Cressie, N.A.C. (1993)
Statistics for Spatial Data.
New York, Wiley.
- 41
-
Creutz, M. (1983)
Quarks, gluons and lattices.
Cambridge: Cambridge University Press.
- 42
-
D'Agostini, G. (1999)
Bayesian Reasoning in High Energy Physics.
-- Principles and Applications --
CERN Yellow Report 99-03
(Available at
http://www-zeus.roma1.infn.it/
agostini/prob+stat.html)
- 43
-
Davis, L. (ed.) (1987)
Genetic Algorithms and Simulated Annealing.
San Mateo, CA: Morgan Kaufmann.
- 44
-
Davis, L. (ed.) (1991)
Handbook of Genetic Algorithms.
New York: Van Nostrand Reinhold.
- 45
-
De Bruijn, N.G. (1981)
Asymptotic Methods in Analysis.
(Originally published in 1958
by the North-Holland Publishing Co., Amsterdam)
New York: Dover.
- 46
-
Deco, G. & Obradovic, D. (1996)
An Information-Theoretic Approach to Neural Computing.
New York: Springer Verlag.
- 47
-
Devroye, L., Györfi, L., & Lugosi, G. (1996)
A Probabilistic Theory of Pattern recognition.
New York: Springer Verlag.
- 48
-
Di Castro, C. & Jona-Lasinio, G. (1976)
The Renormalization Group Approach to Critical Phenomena.
In:
Domb, C. & Green M.S. (eds.)
Phase Transitions and Critical Phenomena.
London: Academic Press.
- 49
-
Dietrich, R., Opper, M., & Sompolinsky, H. (1999)
Statistical Mechanics of Support Vector Networks.
Physical Review Letters 82(14), 2975-2978.
- 50
-
Donoho, D.L. & Johnstone, I.M. (1989)
Projection-based approximation and a duality with kernel methods.
Ann. Statist. 17(1),58-106.
- 51
-
Doob, J.L. (1953)
Stochastic Processes.
(New edition 1990)
New York: Wiley.
- 52
-
Dudley, R.M. (1984)
A course on empirical processes.
Lecture Notes in Mathematics 1097,2-142.
- 53
-
Ebeling, W., Freund, J., & Schweitzer, F. (1998)
Komplexe Strukturen: Entropie und Information.
Stuttgart: Teubner.
- 54
-
Efron, B. & Tibshirani R.J. (1993)
An Introduction to the Bootstrap.
New York: Chapman & Hall.
- 55
-
Eisenberg, J. & Greiner, W. (1972)
Microscopic Theory of the Nucleus.
North-Holland, Amsterdam.
- 56
-
Fernández, R., Fröhlich, J., & Sokal, A.D. (1992)
Random Walks, Critical Phenomena, and Triviality in
Quantum Field Theory.
Berlin: Springer Verlag.
- 57
-
Fletcher, R. (1987)
Practical Methods of Optimization.
New York: Wiley.
- 58
-
Fredholm I. (1903)
Acta Math. 27.
- 59
-
Friedman, J.H. & Tukey, J.W. (1974)
A projection pursuit algorithm for exploratory data analysis.
IEEE Trans. Comput. 24, 1000-1006.
- 60
-
Friedman, J.H. & Stuetzle, W. (1981)
Projection pursuit regression.
J. Am. Statist. Assoc. 76(376), 817-823.
- 61
-
Fukunaga, K. (1990)
Statistical Pattern Recognition.
Boston: Academic Press.
- 62
-
Gardiner, C.W. (1990)
Handbook of Stochastic Methods.
(2nd ed.)
Berlin: Springer Verlag.
- 63
-
Gardner, E. (1987)
Maximum Storage Capacity in Neural Networks.
Europhysics Letters 4 481-485.
- 64
-
Gardner, E. (1988)
The Space of Interactions in Neural Network Models.
Journal of Physics A 21 257-270.
- 65
-
Gardner, E. & Derrida B. (1988)
Optimal Storage Properties of Neural Network Models.
Journal of Physics A 21 271-284.
- 66
-
Geiger, D. & Girosi, F. (1991)
Parallel and Deterministic Algortihms for MRFs:
Surface Reconstruction.
IEEE Trans. on Pattern Analysis and Machine Intelligence
13 (5), 401-412.
- 67
-
Geiger, D. & Yuille, A.L. (1991)
A Common Framework for Image Segmentation.
Int'l J. Computer Vision
6 (3), 227-243.
- 68
-
Gelfand, S.B. & Mitter, S.K. (1993)
On Sampling Methods and Annealing Algorithms.
Markov Random Fields - Theory and Applications.
New York: Academic Press.
- 69
-
Gelman, A., Carlin, J.B., Stern, H.S., & Rubin, D.B. (1995)
Bayesian Data Analysis.
New York: Chapman & Hall.
- 70
-
Geman, S. & Geman, D. (1984)
Stochastic relaxation,
Gibbs distributions and the Bayesian restoration of images.
IEEE Trans. on Pattern Analysis and Machine Intelligence
6, 721-741.
Reprinted in Shafer & Pearl (eds.) (1990)
Readings in Uncertainty Reasoning.
San Mateo, CA: Morgan Kaufmann.
- 71
-
Geman, D. & Reynoids, G. (1992)
Constraint restoration and the Recover of Discontinuities.
IEEE Trans. on Pattern Analysis and Machine Intelligence.
14, 367-383.
- 72
-
Giraud, B.G., Lapedes, A., Liu, L.C., & Lemm, J.C. (1995)
Lorentzian Neural Nets. Neural Networks 8 (5),
757-767.
- 73
-
Girosi, F. (1991)
Models of noise and robust estimates.
A.I.Memo 1287,
Artificial Intelligence Laboratory,
Massachusetts Institute of Technology.
- 74
-
Girosi, F. (1997)
An equivalence between sparse approximation and support vector machines.
A.I. Memo No.1606,
Artificial Intelligence Laboratory,
Massachusetts Institute of Technology.
- 75
-
Girosi, F., Poggio, T., & Caprile, B. (1991)
Extensions of a theory of networks
for approximations and learning:
Outliers and negative examples.
In Lippmann, R., Moody, J., & Touretzky, D. (eds.)
Advances in Neural Information Processing Systems 3,
San Mateo, CA: Morgan Kaufmann.
- 76
-
Girosi, F., Jones, M., & Poggio, T. (1995)
Regularization Theory and Neural Networks Architectures.
Neural Computation 7 (2), 219-269.
- 77
-
Glimm, J. & Jaffe, A. (1987)
Quantum Physics.
A Functional Integral Point of View.
New York: Springer Verlag.
- 78
-
Goeke, K., Cusson, R.Y., Gruemmer, F., Reinhard, P.-G., Reinhardt, H.,
(1983)
Time-Dependent Hartree-Fock and Beyond: A Review.
Prog. Theor. Physics [Suppl.] 74 & 75, 33.
- 79
-
Goldberg, D.E. (1989)
Genetic Algorithms in Search, Optimization, and Machine Learning.
Redwood City, CA: Addison-Wesley.
- 80
-
Golden, R.M. (1996)
Mathematical Methods for Neural Network Analysis
and Design.
Cambridge, MA: MIT Press.
- 81
-
Golup, G., Heath, M., & Wahba, G.(1979)
Generalized cross validation as a method for choosing a good ridge parameter.
Technometrics 21, 215-224.
- 82
-
Good, I.J. & Gaskins, R.A. (1971)
Nonparametric roughness penalties for probability densities.
Biometrika 58, 255-277.
- 83
-
Green, P.J. & Silverman, B.W. (1994)
Nonparametric Regression and Generalized
Linear Models.
London: Chapman & Hall.
- 84
-
Großman, Ch. & Roos H.-G. (1994)
Numerik partieller Differentialgleichungen.
Stuttgart: Teubner.
- 85
-
Gull, S.F. (1988)
Bayesian data analysis - straight line fitting.
In Skilling, J., (ed.)
Maximum Entropy and Bayesian Methods. Cambridge, 511 -518,
Dordrecht: Kluwer.
- 86
-
Gull, S.F. (1989)
Developments in maximum entropy data analysis.
In Skilling, J, (ed.)
Maximum Entropy and Bayesian Methods. Cambridge 1988, 53 - 71,
Dordrecht: Kluwer.
- 87
-
Hackbusch, W. (1985)
Multi-grid Methods and Applications.
New York: Springer Verlag.
- 88
-
Hackbusch, W. (1989)
Integralgleichungen.
Teubner Studienbücher.
Stuttgart: Teubner.
- 89
-
Hackbusch, W. (1993)
Iterative Lösung großer
schwachbesetzter Gleichungssysteme.
Teubner Studienbücher.
Stuttgart: Teubner.
- 90
-
Härdle, W. (1990)
Applied nonparametric regression.
Cambridge: Cambridge University Press.
- 91
-
Hammersley, J.M. & Handscomb, D.C. (1964)
Monte Carlo Methods.
London: Chapman & Hall.
- 92
-
Hastie, T.J. & Tibshirani, R.J. (1986)
Generalized Additive Models.
Statist. Sci. 1, 297-318.
- 93
-
Hastie, T.J. & Tibshirani, R.J. (1987)
Generalized Additive Models: Some applications.
J. Am. Statist. Assoc. 82,371-386.
- 94
-
Hastie, T.J. & Tibshirani, R.J. (1990)
Generalized Additive Models.
London: Chapman & Hall.
- 95
-
Hastings, W.K. (1970)
Monte Carlo sampling methods using Markov chains
and their applications.
Biometrika 57, 97-109.
- 96
-
Hertz, J., Krogh, A. & Palmer, R.G. (1991)
Introduction to the Theory of Neural Computation.
Santa Fe Institute, Lecture Notes Volume I,
Addison-Wesley.
- 97
-
Hilbert, D. & Courant, R. (1989)
Methods of Mathematical Physics.
Vol.1&2,(1st German editions 1924,1937, Springer Verlag)
New York: Wiley.
- 98
-
Holland, J.H. (1975)
Adaption in Natural and Artificial Systems.
University of Michigan Press.
(2nd ed. MIT Press, 1992.)
- 99
-
Horst, R., Pardalos, M., & Thoai, N.V. (1995)
Introduction to Global Optimization.
Dordrecht: Kluwer.
- 100
-
Huber, P.J. (1979)
Robust Smoothing.
In Launer, E. & Wilkinson G. (eds.)
Robustness in Statistics
New York: Academic Press.
- 101
-
Huber, P.J. (1981)
Robust Statistics.
New York: Wiley.
- 102
-
Huber, P.J. (1985)
Projection Pursuit.
Ann. Statist. 13(2),435-475.
- 103
-
Itzkyson, C. & Drouffe, J.-M. (1989)
Statistical Field Theory.
(Vols. 1 and 2)
Cambridge: Cambridge University Press.
- 104
-
Jaynes, E.T. (in preparation)
Probability Theory: The Logic Of Science.
(Available at http://bayes.wustl.edu/etj/prob.html)
- 105
-
Jeffrey, R. (1999).
Probabilistic Thinking.
(Available at http://www.princeton.edu/
bayesway/)
- 106
-
Jeggle, H. (1979)
Nichtlineare Funktionalanalysis.
Stuttgart: Teubner.
- 107
-
Jensen, F.V. (1996)
An Introduction to Bayesian Networks.
New York: Springer Verlag.
- 108
-
Jones, M.C. & Sibson, R. (1987)
What is Projection Pursuit?
J. Roy. Statist. Soc. A 150, 1-36.
- 109
-
Kaku, M. (1993)
Quantum Field Theory.
Oxford: Oxford University Press.
- 110
-
van Kampen, N.G. (1992)
Stochastic Processes in Physics and Chemistry.
Amsterdam: North-Holland.
- 111
-
Kant, I. (1911)
Kritik der reinen Vernunft.(2nd ed.)
Werke, Vol.3
Berlin: Königliche Akademie der Wissenschaften.
- 112
-
Kimmeldorf, G.S. & Wahba, G. (1970)
A correspondence between Bayesian estimation on stochastic processes
and smoothing splines.
Ann. Math. Stat. 41, 495-502.
- 113
-
Kimmeldorf, G.S. & Wahba, G. (1970)
Spline functions and stochastic processes.
Sankhya Ser. A 32, Part 2, 173-180.
- 114
-
Kirkpatrick, S., Gelatt Jr., C.D., & Vecchi, M.P. (1983)
Optimization by Simulated Annealing.
Science 220, 671-680.
- 115
-
Kirsch, A. (1996)
An Introduction to the Mathematical Theory of Inverse Problems.
New York: Springer Verlag.
- 116
-
Kitagawa, G., Gersch, W. (1996)
Smoothness Priors Analysis of Time Series
New York: Springer Verlag.
- 117
-
Kleinert, H.(1993)
Pfadintegrale.
Mannheim: Wissenschaftsverlag.
- 118
-
Klir, G.J. & Yuan, B. (1995)
Fuzzy Sets and Fuzzy Logic.
Prentice Hall.
- 119
-
Klir, G.J. & Yuan, B. (eds.) (1996)
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems.
World Scientific.
- 120
-
Koecher, M. (1985)
Lineare Algebra und analytische Geometrie.
Berlin: Springer Verlag.
- 121
-
Koza, J.R. (1992)
Genetic Programming
Cambridge, MA: MIT Press.
- 122
-
Kullback, S. & Leibler R.A. (1951)
On Information and Sufficiency.
Ann.Math.Statist. 22, 79-86.
- 123
-
Kullback, S. (1951)
Information Theory and Statistics.
New York: Wiley.
- 124
-
Lapedes, A. & Farber, R. (1988)
How neural nets work.
in Neural Information Processing Systems,
D.Z.Anderson, (ed.),442-456.
New York: American Institute of Physics.
- 125
-
Lauritzen, S.L. (1996)
Graphical Models.
Oxford: Clarendon Press.
- 126
-
Lawson, C. & Hanson, R. (1974)
Solving Least Squares Problems.
Englewood Cliffs, NJ: Prentice-Hall.
- 127
-
Le Bellac, M. (1991)
Quantum and Statistical Field Theory.
Oxford Science Publications,
Oxford: Clarendon Press.
- 128
-
Le Cam, L. (1986)
Asymptotic Methods in Statistical Decision Theory.
New York: Springer Verlag.
- 129
-
Leen, T.K. (1995)
From Data Distributions to Regularization in Invariant Learning.
Neural Computation 7, 974-981.
- 130
-
Lemm, J.C. (1995)
Inhomogeneous Random Phase Approximation for Nuclear and Atomic Reactions.
Annals of Physics 244 (1), 136-200, 1995.
- 131
-
Lemm, J.C. (1995)
Inhomogeneous Random Phase Approximation: A Solvable Model.
Annals of Physics 244 (1), 201-238, 1995.
- 132
-
Lemm, J.C. (1996)
Prior Information and Generalized Questions.
A.I.Memo No. 1598, C.B.C.L. Paper No. 141,
Massachusetts Institute of Technology.
(Available at http://pauli.uni-muenster.de/
lemm)
- 133
-
Lemm, J.C. (1998)
How to Implement A Priori Information:
A Statistical Mechanics Approach.
Technical Report MS-TP1-98-12, Münster University,
arXiv:cond-mat/9808039.
- 134
-
Lemm, J.C. (1998)
Fuzzy Interface with Prior Concepts and Non-Convex Regularization.
In Wilfried Brauer (Ed.),
Proceedings of the 5. International Workshop
"Fuzzy-Neuro Systems '98" (FNS '98), March 19-20, 1998, Munich, Germany,
Sankt Augustin: Infix.
- 135
-
Lemm, J.C. (1998)
Quadratic Concepts.
In Niklasson L., Bodén, M., & Ziemke, T. (eds.)
Proceedings of the 8th International Conference
on Artificial Neural Networks. (ICANN98)
New York: Springer Verlag.
- 136
-
Lemm, J.C. (1998)
Fuzzy Rules and Regularization Theory.
In ELITE European Laboratory for Intelligent Techniques Engineering (ed.):
Proceedings of the
6th European Congress on Intelligent Techniques and Soft Computing
(EUFIT '98),
Aachen, Germany, September 7-10, 1998,
Mainz, Aachen.
- 137
-
Lemm, J.C. (1999)
Mixtures of Gaussian Process Priors.
In Proceedings of the Ninth International Conference
on Artificial Neural Networks (ICANN99),
IEEE Conference Publication No. 470.
London: Institution of Electrical Engineers.
- 138
-
Lemm, J.C. (2000)
Inverse Time-dependent Quantum Mechanics.
Technical Report, MS-TP1-00-1, Münster University,
arXiv:quant-ph/0002010.
- 139
-
Lemm, J.C., Beiu, V., & Taylor, J.G. (1995)
Density Estimation as a Preprocessing Step for Constructive Algorithms.
In Kappen B., Gielen, S. (eds.):
Proceedings of the 3rd SNN Neural Network Symposium.
The Netherlands, Nijmegen, 14-15 September 1995,
Berlin, Springer Verlag.
- 140
-
Lemm, J.C., Giraud, B.G., & Weiguny, A. (1990)
Mean field approximation versus exact treatment of
collisions in few-body systems.
Z. Phys. A - Atomic Nuclei 336, 179-188.
- 141
-
Lemm, J.C., Giraud, B.G., & Weiguny, A. (1994)
Beyond the time independent mean field theory for nuclear and atomic reactions:
Inclusion of particle-hole correlations
in a generalized random phase approximation.
Phys. Rev. Lett. 73, 420,
arXiv:nucl-th/9911056.
- 142
-
Lemm, J.C. & Uhlig, J. (1999)
Hartree-Fock Approximation for Inverse Many-Body Problems.
Technical Report, MS-TP1-99-10, Münster University,
arXiv:nucl-th/9908056.
- 143
-
Lemm, J.C., Uhlig J., & Weiguny, A. (2000)
Bayesian Approach to Inverse Quantum Statistics.
Phys. Rev. Lett. 84, 2068.
arXiv:cond-mat/9907013.
- 144
-
Lifshits, M.A. (1995)
Gaussian Random Functions.
Dordrecht: Kluwer.
- 145
-
Loredo T. (1990)
From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics.
In Fougère, P.F. (ed.)
Maximum-Entropy and Bayesian Methods, Dartmouth, 1989, 81-142.
Dordrecht: Kluwer.
(Available at http://bayes.wustl.edu/gregory/gregory.html)
- 146
-
Louis, A.K. (1989)
Inverse und schlecht gestellte Probleme.
Stuttgart: Teubner.
- 147
-
MacKay, D.J.C. (1992)
The evidence framework applied to classification networks.
Neural Computation 4 (5), 720-736.
- 148
-
MacKay, D.J.C. (1992)
A practical Bayesian framework for backpropagation networks.
Neural Computation 4 (3), 448-472.
- 149
-
MacKay, D.J.C. (1994)
Hyperparameters: optimise or integrate out?
In Heidbreder, G. (ed.)
Maximum Entropy and Bayesian Methods, Santa Barbara 1993.
Dordrecht: Kluwer.
- 150
-
MacKay, D.J.C. (1998)
Introduction to Gaussian processes.
In Bishop, C., (ed.)
Neural Networks and Machine Learning.
NATO Asi Series. Series F, Computer and Systems Sciences, Vol. 168.
- 151
-
Marquardt, D.W. (1970)
Generalized Inverses, Ridge Regression, Biased Linear Estimation,
and Nonlinear Regression.
Technometrics 12, 591-613.
- 152
-
Marquardt, D.W. & Snee R.D. (1975)
Ridge Regression in Practice
The American Statistician 29, 3-20.
- 153
-
Marroquin, J.L., Mitter, S., & Poggio, T. (1987)
Probabilistic solution of ill-posed problems in computational vision.
J. Am. Stat. Assoc. 82, 76-89.
- 154
-
McCullagh, P. & Nelder, J.A. (1989)
Generalized Linear Models
London: Chapman & Hall.
- 155
-
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N.,
Teller, A.H., & Teller, E. (1953)
Equation of state calculations by fast computing machines.
Journal of Chemical Physics 21, 1087-1092.
- 156
-
Mezard, M., Parisi, G., & Virasoro, M.A. (1987)
Spin Glass Theory and Beyond.
Singapore: World Scientific.
- 157
-
Michalewicz, Z. (1992)
Genetic Algorithms + Data Structures = Evolution Programs.
Berlin: Springer Verlag.
- 158
-
Michie, D., Spiegelhalter, D.J., & Taylor, C.C. (Eds.) (1994)
Machine Learning, Neural and Statistical Classification.
New York: Ellis Horwood.
- 159
-
Minski, M.L. & Papert, S.A. (1990)
Perceptrons.
(Expanded Edition, Original edition, 1969)
Cambridge, MA: MIT Press.
- 160
-
Mitchell, M. (1996)
An Introduction to Genetic Algorithms.
Cambridge, MA: MIT Press.
- 161
-
Mitchell, A.R. & Griffiths, D.F. (1980)
The Finite Difference Method in Partial Differential Equations.
New York: Wiley.
- 162
-
Molgedey, L. & Schuster, H.G. (1994)
Separation of a mixture of independent signals
using time delayed correlations.
Phys. Rev. Lett. 72(23), 3634-3637.
- 163
-
Montvay, I. & Münster, G. (1994)
Quantum Fields on a Lattice.
Cambridge: Cambridge University Press.
- 164
-
On the Reciprocal of the General Algebraic Matrix.
Moore, E.H. (1920)
Bull.Amer.Math.Soc. 26, 394-395.
- 165
-
Morozov, V.A. (1984)
Methods for Solving Incorrectly Posed problems.
New York: Springer Verlag.
- 166
-
Mosteller, F. & Wallace, D. (1963)
Inference in an authorship problem.
A comparative study of discrimination methods applied to
authorships of the disputed Federalist papers.
J. Amer. Statist. Assoc. 58, 275-309.
- 167
-
Müller, B. & Reinhardt, J. (1991)
Neural Networks.
(2nd printing)
Berlin, Springer Verlag.
- 168
-
Mumford, D. & Shah, J. (1989)
Optimal Approximations by Piecewise Smooth Functions
and Associated Variational Problems.
Comm. Pure Applied Math. 42, 577-684.
- 169
-
Nadaraya, E.A. (1965)
On nonparametric estimates of density functions and regression curves.
Theor.Prob.Appl. 10,186-190.
- 170
-
Neal, R.M. (1996)
Bayesian Learning for Neural Networks.
New York: Springer Verlag.
- 171
-
Neal, R.M. (1997)
Monte Carlo Implementation of Gaussian Process Models
for Bayesian Regression and Classification.
Technical Report No. 9702, Dept. of Statistics,
Univ. of Toronto, Canada.
- 172
-
Negele, J.W. & Orland, H. (1988)
Quantum Many-Particle Systems.
Frontiers In Physics Series (Vol. 68),
Redwood City, CA:
Addison-Wesley.
- 173
-
Nitzberg, M. & Shiota T. (1992)
Nonlinear Image Filtering With Edge and Corner Enhancement.
IEEE Trans. on Pattern Analysis and Machine Intelligence.
14, (8) 862-833.
- 174
-
O'Hagen, A. (1994)
Kendall's advanced theory of statistics,
Vol. 2B: Bayesian inference.
London: Edward Arnold.
- 175
-
Olshausen, B.A. & Field, D.J. (1995)
Natural Image Statistics and Efficient Coding.
Workshop on Information Theory and the Brain,
Sept. 4-5, 1995, University of Stirling.
Proceedings published in
Network 7, 333-339.
- 176
-
Olshausen, B.A. & Field, D.J. (1996)
Emergence of simple-cell receptive field
properties by learning a spares code for natural images.
Nature 381, 607-609.
- 177
-
Opper, M. (1999)
Gaussian Processes for Classification: Mean Field Algorithms.
Tech Report NCRG/1999/030,
Neural Computing Research Group at Aston University, UK.
- 178
-
Opper, M. & Kinzel, W. (1996)
Statistical Mechanics of Generalization.
In Domany, E., van Hemmen, J.L., & Schulten, K. (eds.)
Models of Neural Networks III.
New York: Springer Verlag.
- 179
-
Opper, M., & Winther, O. (1999)
Mean field methods for classification with Gaussian processes.
In Kearns, M.S., Solla, S.S., & Cohn D.A. (eds.)
Advances in Neural Information Processing Systems 11,
309-315,
Cambridge, MA: MIT Press.
- 180
-
Ó Ruanaidh, J.J.K. & Fitzgerald W.J. (1996)
Numerical Bayesian Methods Applied to Signal Processing.
New York: Springer Verlag.
- 181
-
Parzen, E. (1962)
An approach to time series analysis.
Ann.Math.Statist. 32, 951-989.
- 182
-
Parzen, E. (1962)
On the estimation of a probability function and mode.
Ann.Math.Statist. 33(3).
- 183
-
Parzen, E. (1963)
Probability density functionals and reproducing kernel Hilbert spaces.
In Rosenblatt, M.(ed.)
Proc. Symposium on Time Series Analysis, 155-169,
New York: Wiley.
- 184
-
Parzen, E. (1970)
Statistical inference on time series by rkhs methods.
In Pyke, R.(ed.)
Proc. 12th Biennal Seminar, 1-37,
Montreal, Canada: Canadian Mathematical Congress.
- 185
-
Pearl, J. (1988)
Probabilistic Reasoning in Intelligent Systems.
San Mateo, CA: Morgan Kauffmann.
- 186
-
Penrose, R., (1955)
A generalized inverse for matrices.
Proc. Cambridge Philos. Soc. 51, 406-413.
- 187
-
Penrose, R., (1956)
On Best Approximate Solutions of Linear Matrix Equations.
Proc. Cambridge Philos. Soc. 52, 17-19.
- 188
-
Perona, P. & Malik J. (1990)
Scale-Space and Edge Detection Using Anisotropic Diffusion.
IEEE Trans. on Pattern Analysis and Machine Intelligence.
12(7), 629-639.
- 189
-
Perskin, M.E. & Schroeder, D.V. (1995)
An Introduction to Quantum Field Theory.
Reading, MA, Addison-Wesley.
- 190
-
Pierre, D.A. (1986)
Optimization Theory with Applications.
New York: Dover. (Original edition Wiley, 1969).
- 191
-
Poggio, T. & Girosi, F. (1990)
Networks for Approximation and Learning.
Proceedings of the IEEE, Vol 78, No. 9.
- 192
-
Poggio, T., Torre, V., & Koch, C. (1985)
Computational vision and regularization theory.
Nature 317, 314-319.
- 193
-
Polak, E. (1997)
Optimization.
New York: Springer Verlag.
- 194
-
Pollard, D. (1984)
Convergence of Stochastic Processes.
New York: Springer Verlag.
- 195
-
Pordt, A. (1998)
Random Walks in Field Theory
In Meyer-Ortmanns, H, Klümper A. (eds.) (1998)
Field Theoretical Tools for Polymer and Particle Physics.
Berlin: Springer Verlag.
- 196
-
Press, W.H., Teukolsky, S.A., Vetterling, W.T., & Flannery, B.P. (1992)
Numerical Recipes in C.
Cambridge: Cambridge University Press.
- 197
-
Ring, P., & Schuck, P. (1980)
The Nuclear Many-Body Problem.
New York: Springer Verlag.
- 198
-
Ripley, B.D. (1977)
Modelling spatial patterns (with discussion).
Journal of the Royal Statistical Society series B
39, 172-212.
- 199
-
Ripley, B.D. (1987)
Stochastic Simulation.
New York: Wiley.
- 200
-
Ripley, B.D. (1996)
Pattern Recognition and Neural Networks.
Cambridge: Cambridge University Press.
- 201
-
Robert, C.P. (1994)
The Bayesian Choice.
New York: Springer Verlag.
- 202
-
Rodriguez, C.C. (1997)
Cross validated Non Parametric Bayesianism by Markov Chain Monte Carlo.
arXiv:physics/9712041.
- 203
-
Rose, K., Gurewitz, E., & Fox, G.C. (1990)
Statistical mechanics and
phase transitions in clustering. Phys. Rev. Lett. 65,
945-948.
- 204
-
Rothe, H.J. (1992)
Lattice Gauge Theories.
Singapore: World Scientific.
- 205
-
Rumelhart, D.E., McClelland, J.L.,
and the PDP Research Group (1986)
Parallel Distributed Processing, vol.1& 2,
Cambridge, MA: MIT Press.
- 206
-
Ryder, L.H. (1996)
Quantum Field Theory.
Cambridge: Cambridge University Press.
- 207
-
Schervish, M.J. (1995)
Theory of Statistics.
New York: Springer Verlag.
- 208
-
Schölkopf, B., Burges C., & Smola, A. (1998)
Advances in Kernel Methods: Support Vector Machines.
Cambridge, MA: MIT Press.
- 209
-
Schwefel, H.-P. (1995)
Evolution and Optimum Seeking.
New York: Wiley.
- 210
-
Silverman, B.W. (1984)
Spline smoothing: The equivalent variable kernel method.
Ann. Statist. 12, 898-916.
- 211
-
Silverman, B.W. (1986)
Density Estimation for Statistics and Data Analysis.
London: Chapman & Hall.
- 212
-
Sivia, D.S. (1996)
Data Analysis: A Bayesian Tutorial.
Oxford: Oxford University Press.
- 213
-
Skilling, J. (1991)
On parameter estimation and quantified MaxEnt.
In Grandy, W.T. & Schick, L.H. (eds.)
Maximum Entropy and Bayesian Methods. Laramie, 1990, 267 -273,
Dordrecht: Kluwer.
- 214
-
Smola, A.J. & Schölkopf, B., (1998)
From regularization operators to support vector kernels.
In: Jordan, M.I., Kearns, M.J., & Solla S.A. (Eds.):
Advances in Neural Information Processing Systems 10.
Cambridge, MA: MIT Press.
- 215
-
Smola, A.J., Schölkopf, B., & Müller, K-R. (1998)
The connection between regularization operators and support vector kernels.
Neural Networks 11, 637-649.
- 216
-
Stone, M. (1974)
Cross-validation choice and assessment of statistical predictions.
Journal of the Royal Statistical Society B 36, 111-147.
- 217
-
Stone, M. (1977)
An asymptotic equivalence of choice of model by cross-validation
and Akaike's criterion.
Journal of the Royal Statistical Society B 39, 44.
- 218
-
Stone, C.J. (1985)
Additive regression and other nonparametric models.
Ann. Statist. 13,689-705.
- 219
-
Tierney, L. (1994)
Markov chains for exploring posterior distributions (with discussion).
Annals of Statistics 22, 1701-1762.
- 220
-
Tikhonov, A.N. (1963)
Solution of incorrectly formulated problems
and the regularization method.
Soviet Math. Dokl. 4, 1035-1038.
- 221
-
Tikhonov, A.N. & Arsenin, V.Y. (1977)
Solution of Ill-posed Problems.
Washington, DC: W.H.Winston.
- 222
-
Uhlig, J. (2000)
PhD Thesis, Münster University.
- 223
-
Uhlig, J., Lemm, J., & Weiguny, A. (1998)
Mean field methods for atomic and nuclear reactions:
The link between time-dependent and time-independent approaches.
Eur. Phys. A 2, 343-354.
- 224
-
Vapnik, V.N. (1982)
Estimation of dependencies based on empirical data.
New York: Springer Verlag.
- 225
-
Vapnik, V.N. (1995)
The Nature of Statistical Learning Theory.
New York: Springer Verlag.
- 226
-
Vapnik, V.N. (1998)
Statistical Learning Theory.
New York: Wiley.
- 227
-
Vico, G. (1858, original 1710)
De antiquissima Italorum sapientia
Naples: Stamperia de' Classici Latini.
- 228
-
Wahba, G. (1990)
Spline Models for Observational Data.
Philadelphia: SIAM.
- 229
-
Wahba, G. (1997)
Support vector machines, reproducing kernel Hilbert spaces
and the randomized GACV.
Technical Report 984,
University of Wisconsin.
- 230
-
Wahba, G. & Wold, S. 1975)
A completely automatic French curve.
Commun. Statist. 4, 1-17.
- 231
-
Watkin, T.L.H., Rau, A., & Biehl, M. (1993)
The statistical mechanics of learning a rule.
Rev. Mod. Phys. 65, 499-556.
- 232
-
Watzlawick, P. (ed.) (1984)
The Invented Reality.
New York: Norton.
- 233
-
Weinstein, S. (1995)
The Quantum Theory of Fields. Vol.1
Cambridge: Cambridge University Press.
- 234
-
Weinstein, S. (1996)
The Quantum Theory of Fields. Vol.2
Cambridge: Cambridge University Press.
- 235
-
West, M. & Harrison, J. (1997)
Bayesian Forecasting and Dynamic Models.
New York, Springer Verlag.
- 236
-
Williams, C.K.I. & Barber, D. (1998)
Bayesian Classification With Gaussian Processes
IEEE Trans. on Pattern Analysis and Machine Intelligence.
20(12), 1342-1351.
- 237
-
Williams, C.K.I. & Rasmussen, C.E. (1996)
Gaussian Processes for Regression.
in Advances in Neural Information Processing Systems 8,
D.S. Touretzky et al (eds.), 515-520,
Cambridge, MA: MIT Press.
- 238
-
Winkler, G. (1995)
Image Analysis, Random Fields and Dynamic Monte Carlo Methods.
Berlin: Springer Verlag.
- 239
-
Wolpert, D.H. (ed.) (1995)
The Mathematics of Generalization.
The Proceedings of the SFI/CNLS Workshop on Formal Approaches
to Supervised Learning.
Santa Fe Institute, Studies in the Sciences of Complexity.
Reading, MA: Addison-Wesley.
- 240
-
Wolpert, D.H. (1996)
The Lack of A Priori Distinctions between
Learning Algorithms.
Neural Computation 8 (7), 1341-1390.
- 241
-
Wolpert, D.H. (1996)
The Existence of A Priori Distinctions between
Learning Algorithms.
Neural Computation 8 (7), 1391-1420.
- 242
-
Yakowitz, S.J. & Szidarovsky, F. (1985)
A Comparison of Kriging With Nonparametric Regression Methods.
J.Multivariate Analysis. 16, 21-53.
- 243
-
Yuille, A.L., (1990)
Generalized deformable models, statistical physics
and matching problems.
Neural Computation, 2, (1) 1-24.
- 244
-
Yuille, A.L. & Kosowski, J.J. (1994)
Statistical Physics Algorithm That Converge.
Neural Computation 6 (3), 341-356.
- 245
-
Yuille, A.L., Stolorz, P., & Utans, J. (1994)
Statistical Physics, Mixtures of Distributions,
and EM Algorithm.
Neural Computation, 6 (2), 334-340.
- 246
-
Zhu, S.C. & Mumford, D.´ (1997)
Prior Learning and Gibbs Reaction-Diffusion.
IEEE Trans. on Pattern Analysis and Machine Intelligence
19 (11), 1236-1250.
- 247
-
Zhu, S.C. & Yuille, A.L. (1996)
Region Competition: Unifying Snakes, Region Growing,
and Bayes/MDL for Multiband Image Segmentation.
IEEE Trans. on Pattern Analysis and Machine Intelligence
18 (9), 884-900.
- 248
-
Zhu, S.C., Wu, Y.N., & Mumford, D. (1997)
Minimax Entropy principle and Its Application to Texture Modeling.
Neural Computation, 9 (8).
- 249
-
Zinn-Justin, J. (1989)
Quantum Field Theory and Critical Phenomena.
Oxford: Oxford Science Publications.
just for the URZ Printer
Joerg_Lemm
2001-01-21