Robert Jenssen
Robert Jenssen
Professor & Head, Machine Learning Group, Department of Physics and Technology, University of
Verified email at - Homepage
Cited by
Cited by
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
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
R Jenssen, D Roverso
International Journal of Electrical Power & Energy Systems 99, 107-120, 2018
Spectral clustering of polarimetric SAR data with Wishart-derived distance measures
SN Anfinsen, R Jenssen, T Eltoft
Proc. POLinSAR 7, 1-9, 2007
Independent component analysis for texture segmentation
R Jenssen, T Eltoft
Pattern Recognition 36 (10), 2301-2315, 2003
Mean shift spectral clustering
U Ozertem, D Erdogmus, R Jenssen
Pattern Recognition 41 (6), 1924-1938, 2008
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
Recurrent neural networks for short-term load forecasting: an overview and comparative analysis
FM Bianchi, E Maiorino, MC Kampffmeyer, A Rizzi, R Jenssen
Springer, 2017
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
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-Álvarez, SO Skrøvseth, ...
IEEE journal of biomedical and health informatics 20 (5), 1404-1415, 2014
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
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
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
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
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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