Robert Jenssen
Robert Jenssen
Professor & Head, Machine Learning Group, Department of Physics and Technology, University of
Verifisert e-postadresse på - Startside
TittelSitert avÅr
Information theoretic learning: Renyi's entropy and kernel perspectives
JC Principe
Springer Science & Business Media, 2010
Kernel entropy component analysis
R Jenssen
IEEE transactions on pattern analysis and machine intelligence 32 (5), 847-860, 2009
Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks
M Kampffmeyer, AB Salberg, R Jenssen
Proceedings of the IEEE conference on computer vision and pattern …, 2016
Clustering using Renyi's entropy
R Jenssen, KE Hild, D Erdogmus, JC Principe, T Eltoft
Proceedings of the International Joint Conference on Neural Networks, 2003 …, 2003
The Cauchy–Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels
R Jenssen, JC Principe, D Erdogmus, T Eltoft
Journal of the Franklin Institute 343 (6), 614-629, 2006
Independent component analysis for texture segmentation
R Jenssen, T Eltoft
Pattern Recognition 36 (10), 2301-2315, 2003
Spectral clustering of polarimetric SAR data with Wishart-derived distance measures
SN Anfinsen, R Jenssen, T Eltoft
Proc. POLinSAR 7, 1-9, 2007
Mean shift spectral clustering
U Ozertem, D Erdogmus, R Jenssen
Pattern Recognition 41 (6), 1924-1938, 2008
An overview and comparative analysis of recurrent neural networks for short term load forecasting
FM Bianchi, E Maiorino, MC Kampffmeyer, A Rizzi, R Jenssen
arXiv preprint arXiv:1705.04378, 2017
Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
VN Nguyen, R Jenssen, D Roverso
International Journal of Electrical Power & Energy Systems 99, 107-120, 2018
The Laplacian PDF distance: A cost function for clustering in a kernel feature space
R Jenssen, D Erdogmus, J Principe, T Eltoft
Advances in Neural Information Processing Systems, 625-632, 2005
Information cut for clustering using a gradient descent approach
R Jenssen, D Erdogmus, KE Hild II, JC Principe, T Eltoft
Pattern Recognition 40 (3), 796-806, 2007
Some equivalences between kernel methods and information theoretic methods
R Jenssen, T Eltoft, D Erdogmus, JC Principe
Journal of VLSI signal processing systems for signal, image and video …, 2006
Kernel entropy component analysis for remote sensing image clustering
L Gómez-Chova, R Jenssen, G Camps-Valls
IEEE Geoscience and Remote Sensing Letters 9 (2), 312-316, 2011
Optimizing the Cauchy-Schwarz PDF distance for information theoretic, non-parametric clustering
R Jenssen, D Erdogmus, KE Hild, JC Principe, T Eltoft
International Workshop on Energy Minimization Methods in Computer Vision and …, 2005
Time series cluster kernel for learning similarities between multivariate time series with missing data
KØ Mikalsen, FM Bianchi, C Soguero-Ruiz, R Jenssen
Pattern Recognition 76, 569-581, 2018
Support vector feature selection for early detection of anastomosis leakage from bag-of-words in electronic health records
C Soguero-Ruiz, K Hindberg, JL Rojo-Alvarez, SO Skrøvseth, ...
IEEE journal of biomedical and health informatics 20 (5), 1404-1415, 2014
Spectral feature projections that maximize Shannon mutual information with class labels
U Ozertem, D Erdogmus, R Jenssen
Pattern Recognition 39 (7), 1241-1252, 2006
Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods
C Soguero-Ruiz, K Hindberg, I Mora-Jiménez, JL Rojo-Álvarez, ...
Journal of biomedical informatics 61, 87-96, 2016
Kernel maximum entropy data transformation and an enhanced spectral clustering algorithm
R Jenssen, T Eltoft, M Girolami, D Erdogmus
Advances in Neural Information Processing Systems, 633-640, 2007
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