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Alex Smola

 

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Alex Smola

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Technische Berichte (14):

Gretton A, Bousquet O, Smola AJ und Schölkopf B: Measuring Statistical Dependence with Hilbert-Schmidt Norms, 140, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (Juni-2005).
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Gretton A, Smola A, Bousquet O, Herbrich R, Schölkopf B und Logothetis NK: Behaviour and Convergence of the Constrained Covariance, 130, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (Oktober-2004).
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Gretton A, Herbrich R und Smola AJ: The Kernel Mutual Information, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (April-2003).
Schölkopf B, Platt JC und Smola AJ: Kernel method for percentile feature extraction, MSR-TR-2000-22, Microsoft Research, Microsoft Corporation, Redmond WA USA, (Februar-2000).
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ und Williamson RC: Estimating the support of a high-dimensional distribution, MSR-TR-99-87, Microsoft Research, Microsoft Corporation, Redmond WA USA, (November-1999).
Schölkopf B, Shawe-Taylor J, Smola AJ und Williamson RC: Generalization Bounds via Eigenvalues of the Gram matrix, NC2-TR-1999-035, University of London, Royal Holloway College, NeuroCOLT 2, (März-1999).
Smola AJ, Mangasarian OL und Schölkopf B: Sparse Kernel Feature Analysis, 99-04, University of Wisconsin, Data Mining Institute, Madison, (1999).
Smola AJ, Williamson RC und Schölkopf B: Generalization bounds and learning rates for Regularized principal manifolds, NC2-TR-1998-027, University of London, Royal Holloway College, NeuroCOLT 2, (September-1998).
Smola AJ, Mika S und Schölkopf B: Quantization Functionals and Regularized Principal Manifolds, NC2-TR-1998-028, University of London, Royal Holloway College, NeuroCOLT 2, (September-1998).
Smola AJ, Williamson RC und Schölkopf B: Generalization Bounds for Convex Combinations of Kernel Functions, NC2-TR-1998-022, University of London, Royal Holloway College, NeuroCOLT 2, (August-1998).
Williamson RC, Smola AJ und Schölkopf B: Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators, NC-TR-98-019, University of London, Royal Holloway College, NeuroCOLT, (1998).
Saunders C, Stitson MO, Weston J, Bottou L, Schölkopf B und Smola AJ: Support Vector Machine Reference Manual, CSD-TR-98-03, Department of Computer Science, Royal Holloway, University of London, (1998).
Schölkopf B, Smola AJ und Müller K-R: Nonlinear Component Analysis as a Kernel Eigenvalue Problem, 44, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (Dezember-1996).

Poster (2):

Zien A, Rätsch G, Mika S, Schölkopf B, Lemmen C, Smola A, Lengauer T und Müller K-R (Oktober-1999): Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites, German Conference on Bioinformatics (GCB '99), Heidelberg, Germany.
Schölkopf B, Williamson R, Smola AJ und Shawe-Taylor J (März-1999): Single-class Support Vector Machines, Dagstuhl-Seminar on Unsupervised Learning, Dagstuhl, Germany.

Vorträge (2):

Fukumizu K, Gretton A und Smola A (Juli-2008) Invited Lecture: Painless Embeddings of Distributions: the Function Space View, 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland.
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Vishwanathan SVN, Guttman O, Borgwardt K und Smola A (Dezember-2004) Abstract Talk: Kernel Extrapolations for Enzyme Classification, NIPS 2004 Workshop on New Problems and Methods in Computational Biology (MLCB 2004), Vancouver, BC, Canada.
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Last updated: Montag, 22.05.2017