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Alex Smola

 

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Alex Smola

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Bücher (3):

Bakir GH, Hofmann T, Schölkopf B, Smola AJ, Taskar B und Vishwanathan SVN: Predicting Structured Data, 360, MIT Press, Cambridge, MA, USA, (September-2007). ISBN: 0-262-02617-1, Series: Advances in neural information processing systems
Schölkopf B und Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 644, MIT Press, Cambridge, MA, USA, (Dezember-2002). , Series: Adaptive Computation and Machine Learning
Schölkopf B, Burges CJC und Smola AJ: Advances in Kernel Methods: Support Vector Learning, 352, MIT Press, Cambridge, MA, USA, (1999). ISBN: 0-262-19416-3

Artikel (21):

Song L, Smola A, Gretton A, Bedo J und Borgwardt K (Mai-2012) Feature Selection via Dependence Maximization Journal of Machine Learning Research 13 1393-1434.
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Gretton A, Borgwardt K, Rasch M, Schölkopf B und Smola A (März-2012) A Kernel Two-Sample Test Journal of Machine Learning Research 13 723−773.
Thoma M, Cheng H, Gretton A, Han J, Kriegel H-P, Smola AJ, Song L, Yu PS, Yan X und Borgwardt KM (Oktober-2010) Discriminative frequent subgraph mining with optimality guarantees Statistical Analysis and Data Mining 3(5) 302–318.
Hofmann T, Schölkopf B und Smola AJ (Juni-2008) Kernel Methods in Machine Learning Annals of Statistics 36(3) 1171-1220.
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Sonnenburg S, Braun ML, Ong CS, Bengio S, Bottou L, Holmes G, LeCun Y, Müller K-R, Pereira F, Rasmussen CE, Rätsch G, Schölkopf B, Smola A, Vincent P, Weston J und Williamson RC (Oktober-2007) The Need for Open Source Software in Machine Learning Journal of Machine Learning Research 8 2443-2466.
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Vishwanathan SVN, Borgwardt KM, Guttman O und Smola AJ (März-2006) Kernel extrapolation Neurocomputing 69(7-9) 721-729.
Gretton A, Herbrich R, Smola A, Bousquet O und Schölkopf B (Dezember-2005) Kernel Methods for Measuring Independence Journal of Machine Learning Research 6 2075-2129.
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Ong CS, Smola A und Williamson R (Juli-2005) Learning the Kernel with Hyperkernels Journal of Machine Learning Research 6 1043-1071.
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Chalimourda A, Schölkopf B und Smola AJ (März-2005) Experimentally optimal ν in support vector regression for different noise models and parameter settings Neural Networks 18(2) 205-205.
Smola AJ und Schölkopf B (August-2004) A Tutorial on Support Vector Regression Statistics and Computing 14(3) 199-222.
Graf ABA, Smola AJ und Borer S (Mai-2003) Classification in a Normalized Feature Space using Support Vector Machines IEEE Transactions on Neural Networks 14(3) 597-605.
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Mika S, Rätsch G, Weston J, Schölkopf B, Smola AJ und Müller K-R (Mai-2003) Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5) 623-628.
Williamson RC, Smola AJ und Schölkopf B (September-2001) Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators IEEE Transactions on Information Theory 47(6) 2516-2532.
Smola AJ, Mika S, Schölkopf B und Williamson RC (Juni-2001) Regularized principal manifolds Journal of Machine Learning Research 1 179-209.
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ und Williamson RC (März-2001) Estimating the support of a high-dimensional distribution. Neural Computation 13(7) 1443-1471.
Schölkopf B, Smola AJ, Williamson RC und Bartlett PL (Mai-2000) New Support Vector Algorithms Neural Computation 12(5) 1207-1245.
Schölkopf B, Mika S, Burges CJC, Knirsch P, Müller K-R, Rätsch G und Smola AJ (September-1999) Input space versus feature space in kernel-based methods IEEE Transactions On Neural Networks 10(5) 1000-1017.
Schölkopf B, Müller K-R und Smola AJ (September-1999) Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten Informatik - Forschung und Entwicklung 14(3) 154-163.
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Smola AJ und Schölkopf B (September-1998) On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion Algorithmica 22(1-2) 211-231.
Schölkopf B, Smola AJ und Müller K-R (Juli-1998) Nonlinear Component Analysis as a Kernel Eigenvalue Problem Neural Computation 10(5) 1299-1319.
Smola AJ, Schölkopf B und Müller K-R (Juni-1998) The connection between regularization operators and support vector kernels Neural Networks 11(4) 637-649.

Beiträge zu Tagungsbänden (71):

Zhang X, Song L, Gretton A und Smola A (Juni-2009) Kernel Measures of Independence for Non-IID Data In: Advances in neural information processing systems 21, , Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 1937-1944.
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Thoma M, Cheng H, Gretton A, Han J, Kriegel H-P, Smola AJ, Song L, Yu PS, Yan X und Borgwardt KM (Mai-2009) Near-optimal supervised feature selection among frequent subgraphs, Ninth SIAM International Conference on Data Mining (SDM 2009), Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1076-1087.
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Gretton A, Fukumizu K, Teo CH, Song L, Schölkopf B und Smola AJ (September-2008) A Kernel Statistical Test of Independence In: Advances in neural information processing systems 20, , Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007), Curran, Red Hook, NY, USA, 585-592.
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Song L, Smola AJ, Borgwardt K und Gretton A (September-2008) Colored Maximum Variance Unfolding In: Advances in neural information processing systems 20, , Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007), Curran, Red Hook, NY, USA, 1385-1392.
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Song L, Zhang X, Smola A, Gretton A und Schölkopf B (Juli-2008) Tailoring density estimation via reproducing kernel moment matching, 25th International Conference on Machine Learning (ICML 2008), ACM Press, New York, NY, USA, 992-999.
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Smola AJ, Gretton A, Song L und Schölkopf B (Oktober-2007) A Hilbert Space Embedding for Distributions In: Discovery Science, , 10th International Conference on Discovery Science (DS 2007), Springer, Berlin, Germany, 40-41, Series: Lecture Notes in Computer Science ; 4755.
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Smola A, Gretton A, Song L und Schölkopf B (Oktober-2007) A Hilbert Space Embedding for Distributions In: Algorithmic Learning Theory, , 18th International Conference on Algorithmic Learning Theory (ALT 2007), Springer, Berlin, Germany, 13-31, Series: Lecture Notes in Computer Science ; 4754.
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Gretton A, Borgwardt KM, Rasch M, Schölkopf B und Smola A (September-2007) A Kernel Method for the Two-Sample-Problem In: Advances in Neural Information Processing Systems 19, , Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), MIT Press, Cambridge, MA, USA, 513-520.
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Huang J, Smola A, Gretton A, Borgwardt KM und Schölkopf B (September-2007) Correcting Sample Selection Bias by Unlabeled Data In: Advances in Neural Information Processing Systems 19, , Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), MIT Press, Cambridge, MA, USA, 601-608.
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Teo CH, Smola A, Vishwanathan SVN und Le QV (August-2007) A scalable modular convex solver for regularized risk minimization, 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '07), ACM Press, New York, NY, USA, 727-736.
Gretton A, Borgwardt KM, Rasch M, Schölkopf B und Smola AJ (Juli-2007) A Kernel Approach to Comparing Distributions, Twenty-Second AAAI Conference on Artificial Intelligence (IAAI-07), AAAI Press, Menlo Park, CA, USA, 1637-1641.
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Song L, Bedo J, Borgwardt KM, Gretton A und Smola A (Juli-2007) Gene selection via the BAHSIC family of algorithms, 15th International Conference on Intelligent Systems for Molecular Biology (ISMB 2007), Bioinformatics, 23(13), i490-i498.
Song L, Smola AJ, Gretton A und Borgwardt KM (Juni-2007) A Dependence Maximization View of Clustering, 24th Annual International Conference on Machine Learning (ICML 2007), ACM Press, New York, NY, USA, 815-822.
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Song L, Smola AJ, Gretton A, Borgwardt KM und Bedo J (Juni-2007) Supervised Feature Selection via Dependence Estimation, 24th Annual International Conference on Machine Learning (ICML 2007), ACM Press, New York, NY, USA, 823-830.
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Le QV, Smola AJ, Gärtner T und Altun Y (September-2006) Transductive Gaussian Process Regression with Automatic Model Selection In: Machine Learning: ECML 2006, , 17th European Conference on Machine Learning, Springer, Berlin, Germany, 306-317, Series: Lecture Notes in Computer Science ; 4212.
Borgwardt KM, Gretton A, Rasch M, Kriegel H-P, Schölkopf B und Smola A (August-2006) Integrating Structured Biological data by Kernel Maximum Mean Discrepancy, 14th International Conference on Intelligent Systems for Molecular Biology (ISMB 2006), Bioinformatics, 22(14), e49-e57.
McAuley J, Caetano T, Smola A und Franz MO (Juni-2006) Learning High-Order MRF Priors of Color Images, 23rd International Conference on Machine Learning (ICML 2006), ACM Press, New York, NY, USA, 617-624.
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Altun Y und Smola AJ (Juni-2006) Unifying Divergence Minimization and Statistical Inference Via Convex Duality In: Learning Theory, , 19th Annual Conference on Learning Theory (COLT 2006), Springer, Berlin, Germany, 139-153, Series: Lecture Notes in Computer Science ; 4005.
Gretton A, Bousquet O, Smola A und Schölkopf B (Oktober-2005) Measuring Statistical Dependence with Hilbert-Schmidt Norms In: Algorithmic Learning Theory, , 16th International Conference on Algorithmic Learning Theory (ALT 2005), Springer, Berlin, Germany, 63-78, Series: Lecture Notes in Computer Science ; 3734.
Borgwardt KM, Ong CS, Schönauer S, Vishwanathan SVN, Smola AJ und Kriegel H-P (Juni-2005) Protein function prediction via graph kernels, 13th International Conference on Intelligent Systems for Molecular Biology (ISMB 2005), Bioinformatics, 21(Supplement 1), i47-i56.
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Borgwardt KM, Guttman O, Vishwanathan SVN und Smola AJ (April-2005) Joint Regularization, 13th European Symposium on Artificial Neural Networks (ESANN 2005), D-Side, Evere, Belgium, 455-460.
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Gretton A, Smola AJ, Bousquet O, Herbrich R, Belitski A, Augath M, Murayama Y, Pauls J, Schölkopf B und Logothetis NK (Januar-2005) Kernel Constrained Covariance for Dependence Measurement, Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), Society for Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 112-119.
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Altun Y, Smola AJ und Hofmann T (Juli-2004) Exponential Families for Conditional Random Fields, 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004), Morgan Kaufmann, San Francisco, CA, USA, 2-9.
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Altun Y, Hofmann T und Smola AJ (Juli-2004) Gaussian Process Classification for Segmenting and Annotating Sequences, Twenty-first International Conference on Machine Learning (ICML 2004), ACM Press, New York, USA, 4.
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Ong CS, Mary X, Canu S und Smola AJ (Juli-2004) Learning with Non-Positive Kernels, Twenty-first International Conference on Machine Learning (ICML 2004), ACM Press, New York, NY, USA, 81.
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Rätsch G, Smola A und Mika S (Oktober-2003) Adapting Codes and Embeddings for Polychotomies In: Advances in Neural Information Processing Systems 15, , Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002), MIT Press, Cambridge, MA, USA, 513-520.
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Last updated: Montag, 22.05.2017