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Dr. Matthias Hein

 

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Matthias Hein

Position: Wissenschaftler  Abteilung: 

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Artikel (5):

Steinke F, Hein M und Schölkopf B (September-2010) Nonparametric Regression between General Riemannian Manifolds SIAM Journal on Imaging Sciences 3(3) 527-563.
Steinke F, Hein M, Peters J und Schölkopf B (April-2008) Manifold-valued Thin-plate Splines with Applications in Computer Graphics Computer Graphics Forum 27(2) 437-448.
Hein M, Audibert J-Y und von Luxburg U (Juni-2007) Graph Laplacians and their Convergence on Random Neighborhood Graphs Journal of Machine Learning Research 8 1325-1370.
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Hein M, Bousquet O und Schölkopf B (Oktober-2005) Maximal Margin Classification for Metric Spaces Journal of Computer and System Sciences 71(3) 333-359.
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Frauendiener J und Hein M (2002) Numerical evolution of axisymmetric, isolated systems in general relativity Physical Review D 66 124004-124004.
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Beiträge zu Tagungsbänden (11):

von Luxburg U, Radl A und Hein M (Juni-2011) Getting lost in space: Large sample analysis of the resistance distance In: Advances in Neural Information Processing Systems 23, , Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS 2010), Curran, Red Hook, NY, USA, 2622-2630.
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Maier M, von Luxburg U und Hein M (Juni-2009) Influence of graph construction on graph-based clustering measures In: Advances in neural information processing systems 21, , Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 1025-1032.
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Hein M und Maier M (September-2007) Manifold Denoising In: Advances in Neural Information Processing Systems 19, , Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), MIT Press, Cambridge, MA, USA, 561-568.
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Hein M und Maier M (Juli-2007) Manifold Denoising as Preprocessing for Finding Natural Representations of Data, Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07), AAAI Press, Menlo Park, CA, USA, 1646-1649.
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Hein M (Juni-2006) Uniform Convergence of Adaptive Graph-Based Regularization In: Learning Theory, , 19th Annual Conference on Learning Theory (COLT 2006), Springer, Berlin, Germany, 50-64, Series: Lecture Notes in Computer Science ; 4005.
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Hein M und Audibert Y (August-2005) Intrinsic dimensionality estimation of submanifolds in Rd, 22nd International Conference on Machine Learning (ICML 2005), ACM Press, New York, NY, USA, 289-296.
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Hein M, Audibert J-Y und von Luxburg U (Juni-2005) From Graphs to Manifolds: Weak and Strong Pointwise Consistency of Graph Laplacians In: Learning Theory, , 18th Annual Conference on Learning Theory (COLT 2005), Springer, Berlin, Germany, 470-485, Series: Lecture Notes in Computer Science ; 3559.
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Hein M und Bousquet O (Januar-2005) Hilbertian Metrics and Positive Definite Kernels on Probability Measures, Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), Society for Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 136-143.
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Hein H, Lal TN und Bousquet O (September-2004) Hilbertian Metrics on Probability Measures and their Application in SVM's In: Pattern Recognition, , 26th Annual Symposium of the German Association for Pattern Recognition (DAGM 2004), Springer, Berlin, Germany, 270-277, Series: Lecture Notes in Computer Science ; 3175.
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Bousquet O, Chapelle O und Hein M (Juni-2004) Measure Based Regularization In: Advances in Neural Information Processing Systems 16, , Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), MIT Press, Cambridge, MA, USA, 1221-1228.
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Hein M und Bousquet O (August-2003) Maximal Margin Classification for Metric Spaces In: Learning Theory and Kernel Machines, , 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), Springer, Berlin, Germany, 72-86, Series: Lecture Notes in Computer Science ; 2777.

Technische Berichte (3):

Hein M, Steinke F und Schölkopf B: Energy Functionals for Manifold-valued Mappings and Their Properties, 167, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (Januar-2008).
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Hein M und Bousquet O: Hilbertian Metrics and Positive Definite Kernels on Probability Measures, 126, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (Juli-2004).
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Hein M und Bousquet O: Kernels, Associated Structures and Generalizations, 127, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (Juli-2004).
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Abschlussarbeiten (1):

Hein M: Geometrical aspects of statistical learning theory, Technische Universität Darmstadt, (November-2005). PhD thesis
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Vorträge (1):

Steinke F, Hein M und Schölkopf B (Juni-2008) Invited Lecture: Thin-Plate Splines Between Riemannian Manifolds, Workshop on Geometry and Statistics of Shapes 2008, Bonn, Germany.

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Last updated: Montag, 22.05.2017