Maschinelles Lernen durch Funktionsrekonstruktion mit verallgemeinerten dGittern
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[1] Thomas I. Seidman,et al. Nonconvergence results for the application of least-squares estimation to Ill-posed problems , 1980 .
[2] Martin Hanke,et al. On Lanczos Based Methods for the Regularization of Discrete Ill-Posed Problems , 2001 .
[3] Ralf Hiptmair,et al. Multigrid for Discrete Differential Forms on Sparse Grids , 2003, Computing.
[4] Andreas Rieder,et al. A wavelet multilevel method for ill-posed problems stabilized by Tikhonov regularization , 1997 .
[5] John Quackenbush,et al. Computational genetics: Computational analysis of microarray data , 2001, Nature Reviews Genetics.
[6] Michael Griebel,et al. On the Parallelization of the Sparse Grid Approach for Data Mining , 2001, LSSC.
[7] Glenn Fung,et al. Proximal support vector machine classifiers , 2001, KDD '01.
[8] F. Delvos. d-Variate Boolean interpolation , 1982 .
[9] Tomaso A. Poggio,et al. Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.
[10] Michael Griebel,et al. Multiscale Methods for the Simulation of Turbulent Flows , 2003 .
[11] Ian H. Witten,et al. Data mining : praktische Werkzeuge und Techniken für das maschinelle Lernen , 2001 .
[12] H. Freudenthal. Simplizialzerlegungen von Beschrankter Flachheit , 1942 .
[13] Henryk Wozniakowski,et al. Weighted Tensor Product Algorithms for Linear Multivariate Problems , 1999, J. Complex..
[14] W. Dahmen. Wavelet and multiscale methods for operator equations , 1997, Acta Numerica.
[15] William D. Penny,et al. Bayesian neural networks for classification: how useful is the evidence framework? , 1999, Neural Networks.
[16] E. Arge,et al. Approximation of scattered data using smooth grid functions , 1995 .
[17] A. Louis. Inverse und schlecht gestellte Probleme , 1989 .
[18] Michael Griebel,et al. Sparse grids for boundary integral equations , 1999, Numerische Mathematik.
[19] Per Christian Hansen,et al. Rank-Deficient and Discrete Ill-Posed Problems , 1996 .
[20] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.
[21] J. Friedman. Multivariate adaptive regression splines , 1990 .
[22] David Haussler,et al. Classifying G-protein coupled receptors with support vector machines , 2002, Bioinform..
[23] V. N. Temli︠a︡kov. Approximation of periodic functions , 1993 .
[24] H. Engl,et al. Regularization of Inverse Problems , 1996 .
[25] Richard Uden,et al. Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps , 2002 .
[26] M. Griebel,et al. Optimized Tensor-Product Approximation Spaces , 2000 .
[27] Arnold Neumaier,et al. Solving Ill-Conditioned and Singular Linear Systems: A Tutorial on Regularization , 1998, SIAM Rev..
[28] A. N. Tikhonov,et al. Solutions of ill-posed problems , 1977 .
[29] Claes Johnson,et al. Introduction to Adaptive Methods for Differential Equations , 1995, Acta Numerica.
[30] M. Hegland. Adaptive sparse grids , 2003 .
[31] M. Griebel,et al. On the computation of the eigenproblems of hydrogen helium in strong magnetic and electric fields with the sparse grid combination technique , 2000 .
[32] Felipe Cucker,et al. Best Choices for Regularization Parameters in Learning Theory: On the Bias—Variance Problem , 2002, Found. Comput. Math..
[33] Kurt Hornik,et al. The support vector machine under test , 2003, Neurocomputing.
[34] Thomas Gerstner,et al. Numerical integration using sparse grids , 2004, Numerical Algorithms.
[35] Wolfgang Dahmen,et al. On the regularization of dynamic data reconciliation problems , 2002 .
[36] Irfan Altas,et al. Approximation of a Thin Plate Spline Smoother Using Continuous Piecewise Polynomial Functions , 2003, SIAM J. Numer. Anal..
[37] Gene H. Golub,et al. Scientific computing , 1993 .
[38] Federico Girosi,et al. An Equivalence Between Sparse Approximation and Support Vector Machines , 1998, Neural Computation.
[39] G. Wahba. Spline models for observational data , 1990 .
[40] F. Girosi,et al. From regularization to radial, tensor and additive splines , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).
[41] Hans-Joachim Bungartz,et al. A Note on the Complexity of Solving Poisson's Equation for Spaces of Bounded Mixed Derivatives , 1999, J. Complex..
[42] T. Poggio,et al. The Mathematics of Learning: Dealing with Data , 2005, 2005 International Conference on Neural Networks and Brain.
[43] Aihui Zhou,et al. Error analysis of the combination technique , 1999, Numerische Mathematik.
[44] Witold Pedrycz,et al. Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.
[45] K. Stuben,et al. Algebraic Multigrid (AMG) : An Introduction With Applications , 2000 .
[46] Henryk Wozniakowski,et al. When Are Quasi-Monte Carlo Algorithms Efficient for High Dimensional Integrals? , 1998, J. Complex..
[47] Wolfgang Marquardt,et al. Stepwise Refinement of Sparse Grids in Data Mining Applications , 2003 .
[48] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[49] Robert Plato,et al. On the regularization of projection methods for solving III-posed problems , 1990 .
[50] Angela Kunoth,et al. Multilevel regularization of wavelet based fitting of scattered data – some experiments , 2005, Numerical Algorithms.
[51] V. N. Temli︠a︡kov. Approximation of functions with bounded mixed derivative , 1989 .
[52] Linda Kaufman,et al. Solving the quadratic programming problem arising in support vector classification , 1999 .
[53] Eric R. Ziegel,et al. Mastering Data Mining , 2001, Technometrics.
[54] Serguei Dachkovski,et al. Anisotropic function spaces and related semi–linear hypoelliptic equations , 2003 .
[55] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[56] S. Saitoh. Integral Transforms, Reproducing Kernels and Their Applications , 1997 .
[57] C. Schwab,et al. NUMERICAL SOLUTION OF PARABOLIC EQUATIONS IN HIGH DIMENSIONS , 2004 .
[58] G. Baszenski,et al. Blending Approximations with Sine Functions , 1992 .
[59] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[60] A. Tikhonov. On the stability of inverse problems , 1943 .
[61] V. Vapnik. Estimation of Dependences Based on Empirical Data , 2006 .
[62] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[63] Pieter W. Hemker,et al. Application of an Adaptive Sparse-Grid Technique to a Model Singular Perturbation Problem , 2000, Computing.
[64] Jean Duchon,et al. Splines minimizing rotation-invariant semi-norms in Sobolev spaces , 1976, Constructive Theory of Functions of Several Variables.
[65] Thomas Gerstner,et al. Dimension–Adaptive Tensor–Product Quadrature , 2003, Computing.
[66] Rüdiger Verfürth,et al. A posteriori error estimation and adaptive mesh-refinement techniques , 1994 .
[67] A. Kirsch. An Introduction to the Mathematical Theory of Inverse Problems , 1996, Applied Mathematical Sciences.
[68] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.
[69] Michael Griebel,et al. Turbulence Simulation on Sparse Grids Using the Combination Method , 1994 .
[70] Michael Griebel,et al. The efficient solution of fluid dynamics problems by the combination technique , 1995, Forschungsberichte, TU Munich.
[71] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[72] Barbara Kaltenbacher,et al. Regularization by projection with a posteriori discretization level choice for linear and nonlinear ill-posed problems , 2000 .
[73] Michel Fortin,et al. Mixed and Hybrid Finite Element Methods , 2011, Springer Series in Computational Mathematics.
[74] Christoph Schwab,et al. Sparse finite elements for elliptic problems with stochastic loading , 2003, Numerische Mathematik.
[75] Jing Peng,et al. SVM vs regularized least squares classification , 2004, ICPR 2004.
[76] W. Dahmen,et al. Multilevel preconditioning , 1992 .
[77] Christoph Schwab,et al. Sparse Finite Elements for Stochastic Elliptic Problems – Higher Order Moments , 2003, Computing.
[78] Brian D. Ripley,et al. Neural Networks and Related Methods for Classification , 1994 .
[79] A Tikhonov,et al. Solution of Incorrectly Formulated Problems and the Regularization Method , 1963 .
[80] Harold W. Kuhn,et al. Some Combinatorial Lemmas in Topology , 1960, IBM J. Res. Dev..
[81] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[82] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[83] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[84] S. Odewahn,et al. Automated star/galaxy discrimination with neural networks , 1992 .
[85] S. Achatz,et al. Higher Order Sparse Grid Methods for Elliptic Partial Differential Equations with Variable Coefficients , 2003, Computing.
[86] Michael Griebel,et al. Tensor product type subspace splittings and multilevel iterative methods for anisotropic problems , 1995, Adv. Comput. Math..
[87] Vladimir N. Temlyakov,et al. On Approximate Recovery of Functions with Bounded Mixed Derivative , 1993, J. Complex..
[88] Harry Yserentant,et al. Hierarchical bases , 1992 .
[89] A. Lopez-Molinero,et al. Classification of ancient Roman glazed ceramics using the neural network of Self-Organizing Maps , 2000, Fresenius' journal of analytical chemistry.
[90] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[91] Frank Natterer,et al. Regularisierung schlecht gestellter Probleme durch Projektionsverfahren , 1977 .
[92] Fred J. Hickernell,et al. Integration and approximation in arbitrary dimensions , 2000, Adv. Comput. Math..
[93] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[94] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[95] V. A. Morozov,et al. Methods for Solving Incorrectly Posed Problems , 1984 .
[96] H. Yserentant. On the multi-level splitting of finite element spaces , 1986 .
[97] Sergei V. Pereverzev,et al. Self-regularization of projection methods with a posteriori discretization level choice for severely ill-posed problems , 2003 .
[98] U. Rüde,et al. Extrapolation, combination, and sparse grid techniques for elliptic boundary value problems , 1992, Forschungsberichte, TU Munich.
[99] Ilse C. F. Ipsen,et al. The Lack of Influence of the Right-Hand Side on the Accuracy of Linear System Solution , 1998, SIAM J. Sci. Comput..
[100] S. Canu,et al. M L ] 6 O ct 2 00 9 Functional learning through kernel , 2009 .
[101] Per Christian Hansen,et al. Analysis of Discrete Ill-Posed Problems by Means of the L-Curve , 1992, SIAM Rev..
[102] Peter J. Rousseeuw,et al. Robust regression and outlier detection , 1987 .
[103] David L. Phillips,et al. A Technique for the Numerical Solution of Certain Integral Equations of the First Kind , 1962, JACM.
[104] Felipe Cucker,et al. On the mathematical foundations of learning , 2001 .
[105] Pieter W. Hemker. Sparse-grid finite-volume multigrid for 3D-problems , 1995, Adv. Comput. Math..
[106] Karin Frank,et al. Information Complexity of Multivariate Fredholm Integral Equations in Sobolev Classes , 1996, J. Complex..
[107] W. Rheinboldt,et al. Error Estimates for Adaptive Finite Element Computations , 1978 .
[108] G. Baszenski. n-th Order Polynomial Spline Blending , 1985 .
[109] Michael Griebel,et al. The Combination Technique for the Sparse Grid Solution of PDE's on Multiprocessor Machines , 1992, Parallel Process. Lett..
[110] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[111] Bernhard Schölkopf,et al. The connection between regularization operators and support vector kernels , 1998, Neural Networks.
[112] Rolf Rannacher,et al. An optimal control approach to a posteriori error estimation in finite element methods , 2001, Acta Numerica.