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Bob Williamson

 

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Bob Williamson

Position: Wissenschaftler  Abteilung: 

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Artikel (6):

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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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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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.

Beiträge zu Tagungsbänden (14):

von Luxburg U, Williamson RC und Guyon I (2012) Clustering: Science or Art?, Unsupervised and Transfer Learning Workshop, held at ICML 2011, International Machine Learning Society, Madison, WI, USA, 65-80, Series: JMLR Workshop and Conference Proceedings ; 27.
Guyon I, von Luxburg U und Williamson R (Dezember-2009) Clustering: Science or Art?, NIPS 2009 Workshop on "Clustering: Science or Art? Towards Principled Approaches", 1-11.
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Ong CS, Smola AJ und Williamson RC (Oktober-2003) Hyperkernels In: Advances in Neural Information Processing Systems 15, , Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002), MIT Press, Cambridge, MA, USA, 495-502.
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Williamson RC, Smola AJ und Schölkopf B (Juli-2000) Entropy Numbers of Linear Function Classes., 13th Annual Conference on Computational Learning Theory (COLT 2000), Morgan Kaufmann, San Francisco, CA, USA, 309-319.
Schölkopf B, Williamson RC, Smola AJ, Shawe-Taylor J und Platt JC (Juni-2000) Support vector method for novelty detection In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 582-588.
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Smola AJ, Shawe-Taylor J, Schölkopf B und Williamson RC (Juni-2000) The entropy regularization information criterion In: Advances in Neural Information Processing Systems 12, , Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999), MIT Press, Cambridge, MA, USA, 342-348.
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Smola AJ, Elisseeff A, Schölkopf B und Williamson RC (2000) Entropy numbers for convex combinations and MLPs In: Advances in Large Margin Classifiers, , NIPS 1998 Workshop “Advances in Large Margin Classifiers”, MIT Press, Cambridge, MA, USA, 369-387, Series: Neural Information Processing Series.
Graepel T, Herbrich R, Schölkopf B, Smola AJ, Bartlett P, Müller K, Obermayer K und Williamson RC (September-1999) Classification on proximity data with LP-machines, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 304-309, Series: Conference Publication of the Institution of Electrical Engineers ; 470.
Schölkopf B, Shawe-Taylor J, Smola AJ und Williamson RC (September-1999) Kernel-dependent support vector error bounds, Ninth International Conference on Artificial Neural Networks (ICANN 99), Institute of Electrical Engineers, London, UK, 103-108, Series: Conference Publication of the Institution of Electrical Engineers ; 470.
Schölkopf B, Bartlett PL, Smola AJ und Williamson R (Juni-1999) Shrinking the tube: a new support vector regression algorithm In: Advances in Neural Information Processing Systems 11, , Twelfth Annual Conference on Neural Information Processing Systems (NIPS 1998), MIT Press, Cambridge, MA, USA, 330-336.
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Williamson RC, Smola AJ und Schölkopf B (März-1999) Entropy numbers, operators and support vector kernels In: Computational Learning Theory, , 4th European Conference on Computational Learning Theory (EuroCOLT’99), Springer, Berlin, Germany, 285-299, Series: Lecture Notes in Artificial Intelligence ; 1572.
Smola AJ, Williamson RC, Mika S und Schölkopf B (März-1999) Regularized principal manifolds In: Computational Learning Theory, , 4th European Conference on Computational Learning Theory (EuroCOLT’99), Springer, Berlin, Germany, 214-229, Series: Lecture Notes in Artificial Intelligence ; 1572.
Williamson RC, Smola AJ und Schölkopf B (1999) Entropy numbers, operators and support vector kernels. In: Advances in kernel methods: support vector learning, , Eleventh Annual Conference on Neural Information Processing (NIPS 1997), MIT Press, Cambridge, MA, 127-144.
Schölkopf B, Bartlett P, Smola AJ und Williamson R (September-1998) Support vector regression with automatic accuracy control. In: ICANN 98, , 8th International Conference on Artificial Neural Networks, Springer, Berlin, Germany, ICANN'98, 111-116, Series: Perspectives in Neural Computing.

Technische Berichte (5):

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, 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, 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).

Poster (1):

Schölkopf B, Williamson R, Smola AJ und Shawe-Taylor J (März-1999): Single-class Support Vector Machines, Dagstuhl-Seminar on Unsupervised Learning, Dagstuhl, Germany.

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Last updated: Montag, 22.05.2017