Conference papers (10): |
|---|
|
•
|
Dinuzzo F  , Ong CS , Gehler PV and Pillonetto G (2011) Learning output kernels with block coordinate descent
(Ed) Getoor, L. , T. Scheffer, 28th International Conference on Machine Learning (ICML 2011), International Machine Learning Society, Madison, WI, USA, 49-56.
 
|
|
•
|
Gehler P , Rother C , Kiefel M , Zhang L and Schölkopf B (2011) Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
In: Advances in Neural Information Processing Systems 24, (Ed) J Shawe-Taylor, RS Zemel, PL Bartlett, FCN Pereira and KQ Weinberger, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Curran Associates, Inc., Red Hook, NY, USA, 765-773.
|
|
•
|
Gehler PV and Nowozin S (2009) On Feature Combination for Multiclass Object Classification
In: ICCV 2009, Twelfth IEEE International Conference on Computer Vision, IEEE Computer Society, Piscataway, NJ, USA, 221-228.
|
|
•
|
Gehler PV and Nowozin S (2009) Let the Kernel Figure it Out: Principled Learning of Pre-processing for Kernel Classifiers
In: CVPR 2009, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Service Center, Piscataway, NJ, USA, 2836-2843.
|
|
•
|
Gehler PV and Nowozin S (2008) Infinite Kernel Learning
NIPS 2008 Workshop on "Kernel Learning: Automatic Selection of Optimal Kernels" (LK ASOK´08), 1-4.
|
|
•
|
Gehler PV , Rother C , Blake A , Minka T and Sharp T (2008) Bayesian Color Constancy Revisited
In: CVPR 2008, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Service Center, Piscataway, NJ, USA, 1-8.
|
|
•
|
Gehler PV and Chapelle O (2007) Deterministic Annealing for
Multiple-Instance Learning
In: JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007, (Ed) Meila, M. , X. Shen, 11th International Conference on Artificial Intelligence and Statistics, MIT Press, Cambridge, MA, USA, 123-130.
|
|
•
|
Franz MO and Gehler PV (2006) How to choose the covariance for Gaussian process regression independently of the basis
Workshop Gaussian Processes in Practice (GPIP 2006), videolectures.net, Scottsdale, AZ, USA, -, 1-4.
|
|
•
|
Gehler PV , Holub AD and Welling M (2006) The Rate Adapting Poisson Model for Information Retrieval and Object Recognition
In: ICML 2006, (Ed) Cohen, W. W., A. Moore, 23rd International Conference on Machine Learning, ACM Press, New York, NY, USA, 337-344.
|
|
•
|
Gehler PV and Welling M (2006) Products of "Edge-perts"
In: Advances in neural information processing systems 18, (Ed) Weiss, Y. , B. Schölkopf, J. Platt, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005), MIT Press, Cambridge, MA, USA, 419-426.
|
Contributions to books (1): |
|---|
|
•
|
Gehler PV and Schölkopf B : An introduction to kernel learning algorithms, 25-48.
In: Kernel Methods for Remote Sensing Data Analysis, (Ed) G Camps-Valls and L Bruzzone, Wiley, New York, NY, USA, (2009).
|
Technical reports (2): |
|---|
|
•
|
Gehler PV and Nowozin S : Infinite Kernel Learning, 178, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, (2008).
|
|
•
|
Gehler PV and Franz M : Implicit Wiener Series, Part II: Regularised estimation, 148, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (2006).
|
Theses (1): |
|---|
|
•
|
Gehler PV : Kernel Learning Approaches for Image Classification, Universität des Saarlandes, Saarbrücken, Germany, (2009).
PhD thesis
|
pgehler
