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ORCIDMarco Loog
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- affiliation: Delft University of Technology, Netherlands
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2020 – today
- 2023
[j60]Marco Loog
, Jesse H. Krijthe, Manuele Bicego:
Also for k-means: more data does not imply better performance. Mach. Learn. 112(8): 3033-3050 (2023)- [j59]Alexander Mey, Marco Loog:
Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4747-4767 (2023) - [j58]Tom J. Viering, Marco Loog:
The Shape of Learning Curves: A Review. IEEE Trans. Pattern Anal. Mach. Intell. 45(6): 7799-7819 (2023) - [j57]Rolf A. N. Starre, Marco Loog, Elena Congeduti, Frans A. Oliehoek:
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations. Trans. Mach. Learn. Res. 2023 (2023) - [c129]Yuko Kato, David M. J. Tax, Marco Loog:
A Review of Nonconformity Measures for Conformal Prediction in Regression. COPA 2023: 369-383 - [c128]Chirag Raman, Alec Nonnemaker, Amelia Villegas-Morcillo, Hayley Hung, Marco Loog:
Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models. NeurIPS 2023 - [c127]Soufiane Mourragui, Marco Loog, Mirrelijn M. van Nee, Mark A. van de Wiel, Marcel J. T. Reinders, Lodewyk F. A. Wessels:
Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response. RECOMB 2023: 120-138 - 2022
- [j56]Yazhou Yang, Marco Loog:
To Actively Initialize Active Learning. Pattern Recognit. 131: 108836 (2022) - [c126]Zhiyi Chen, Marco Loog, Jesse H. Krijthe:
Explaining Two Strange Learning Curves. BNAIC/BENELEARN 2022: 16-30 - [c125]Yuko Kato, David M. J. Tax, Marco Loog:
A View on Model Misspecification in Uncertainty Quantification. BNAIC/BENELEARN 2022: 65-77 - [c124]Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek:
Model-Based Reinforcement Learning with State Abstraction: A Survey. BNAIC/BENELEARN 2022: 133-148 - [c123]Ziqi Wang, Marco Loog:
Enhancing Classifier Conservativeness and Robustness by Polynomiality. CVPR 2022: 13317-13326 - [c122]Chirag Raman, Hayley Hung, Marco Loog:
Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues. ECCV Workshops (3) 2022: 639-659 - [c121]Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn:
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. ECML/PKDD (5) 2022: 3-19 - [i56]Ziqi Wang, Marco Loog:
Enhancing Classifier Conservativeness and Robustness by Polynomiality. CoRR abs/2203.12693 (2022) - [i55]Chirag Raman, Hayley Hung, Marco Loog:
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts. CoRR abs/2206.00679 (2022) - [i54]Gijs van Tulder, Marco Loog:
On the reusability of samples in active learning. CoRR abs/2206.06276 (2022) - [i53]Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek:
An Analysis of Abstracted Model-Based Reinforcement Learning. CoRR abs/2208.14407 (2022) - [i52]Yuko Kato, David M. J. Tax, Marco Loog:
A view on model misspecification in uncertainty quantification. CoRR abs/2210.16938 (2022) - [i51]Marco Loog, Tom J. Viering:
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance. CoRR abs/2211.14061 (2022) - 2021
- [j55]Wouter M. Kouw, Marco Loog:
A Review of Domain Adaptation without Target Labels. IEEE Trans. Pattern Anal. Mach. Intell. 43(3): 766-785 (2021) - [j54]Wouter M. Kouw, Marco Loog:
Robust domain-adaptive discriminant analysis. Pattern Recognit. Lett. 148: 107-113 (2021) - [j53]Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert:
Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory. IEEE Trans. Image Process. 30: 8342-8353 (2021) - [c120]Alexander Mey, Marco Loog:
Consistency and Finite Sample Behavior of Binary Class Probability Estimation. AAAI 2021: 8967-8974 - [c119]Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert:
ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility. RRPR 2021: 3-11 - [i50]Marco Loog:
Nearest Neighbor-based Importance Weighting. CoRR abs/2102.02291 (2021) - [i49]Tom J. Viering, Marco Loog:
The Shape of Learning Curves: a Review. CoRR abs/2103.10948 (2021) - [i48]Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert:
Resolution learning in deep convolutional networks using scale-space theory. CoRR abs/2106.03412 (2021) - [i47]Chirag Raman, Hayley Hung, Marco Loog:
Social Processes: Self-Supervised Forecasting of Nonverbal Cues in Social Conversations. CoRR abs/2107.13576 (2021) - 2020
- [c118]Kanav Anand, Ziqi Wang, Marco Loog, Jan van Gemert:
Black Magic in Deep Learning: How Human Skill Impacts Network Training. BMVC 2020 - [c117]Ziqi Wang, Marco Loog, Jan van Gemert:
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. ICPR 2020: 9756-9763 - [c116]Kasra Arnavaz, Aasa Feragen, Oswin Krause, Marco Loog:
Bayesian Active Learning for Maximal Information Gain on Model Parameters. ICPR 2020: 10524-10531 - [c115]Alexander Mey, Tom Julian Viering, Marco Loog:
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. IDA 2020: 326-338 - [c114]Tom Julian Viering, Alexander Mey, Marco Loog:
Making Learners (More) Monotone. IDA 2020: 535-547 - [c113]Wouter M. Kouw, Marco Loog:
Target Robust Discriminant Analysis. S+SSPR 2020: 3-13 - [c112]Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf:
Semi-supervised learning, causality, and the conditional cluster assumption. UAI 2020: 1-10 - [i46]Marco Loog, Tom J. Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax:
A Brief Prehistory of Double Descent. CoRR abs/2004.04328 (2020) - [i45]Kanav Anand, Ziqi Wang, Marco Loog, Jan van Gemert:
Black Magic in Deep Learning: How Human Skill Impacts Network Training. CoRR abs/2008.05981 (2020) - [i44]Ziqi Wang, Marco Loog, Jan van Gemert:
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. CoRR abs/2010.07591 (2020) - [i43]Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert:
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility. CoRR abs/2012.01172 (2020)
2010 – 2019
- 2019
- [j52]Soufiane Mourragui, Marco Loog, Mark A. van de Wiel, Marcel J. T. Reinders, Lodewyk F. A. Wessels:
PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. Bioinform. 35(14): i510-i519 (2019) - [j51]Tom J. Viering, Jesse H. Krijthe, Marco Loog:
Nuclear discrepancy for single-shot batch active learning. Mach. Learn. 108(8-9): 1561-1599 (2019) - [j50]Yuan Zeng, Jan C. A. van der Lubbe, Marco Loog:
Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh's drawings. Mach. Vis. Appl. 30(7-8): 1229-1241 (2019) - [j49]Yazhou Yang, Marco Loog:
Single shot active learning using pseudo annotators. Pattern Recognit. 89: 22-31 (2019) - [j48]Lorenzo Bottarelli, Marco Loog:
Gaussian process variance reduction by location selection. Pattern Recognit. Lett. 125: 727-734 (2019) - [j47]Antonella Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax:
A dissimilarity-based multiple instance learning approach for protein remote homology detection. Pattern Recognit. Lett. 128: 231-236 (2019) - [c111]Julius von Kügelgen, Alexander Mey, Marco Loog:
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. AISTATS 2019: 1361-1369 - [c110]Tom J. Viering, Alexander Mey, Marco Loog:
Open Problem: Monotonicity of Learning. COLT 2019: 3198-3201 - [c109]Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik:
Learning An Mr Acquisition-Invariant Representation Using Siamese Neural Networks. ISBI 2019: 364-367 - [c108]Wouter M. Kouw, Jesse H. Krijthe, Marco Loog:
Robust Importance-Weighted Cross-Validation Under Sample Selection Bias. MLSP 2019: 1-6 - [c107]Marco Loog, Tom J. Viering, Alexander Mey:
Minimizers of the Empirical Risk and Risk Monotonicity. NeurIPS 2019: 7476-7485 - [c106]Mina Sheikhalishahi, Majid Nateghizad, Fabio Martinelli, Zekeriya Erkin, Marco Loog:
On the Statistical Detection of Adversarial Instances over Encrypted Data. STM 2019: 71-88 - [i42]Wouter M. Kouw, Marco Loog:
A review of single-source unsupervised domain adaptation. CoRR abs/1901.05335 (2019) - [i41]Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf:
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR abs/1905.12081 (2019) - [i40]Alexander Mey, Tom J. Viering, Marco Loog:
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. CoRR abs/1906.06100 (2019) - [i39]Marco Loog, Tom J. Viering, Alexander Mey:
Minimizers of the Empirical Risk and Risk Monotonicity. CoRR abs/1907.05476 (2019) - [i38]Tom J. Viering, Ziqi Wang, Marco Loog, Elmar Eisemann:
How to Manipulate CNNs to Make Them Lie: the GradCAM Case. CoRR abs/1907.10901 (2019) - [i37]Alexander Mey, Marco Loog:
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results. CoRR abs/1908.09574 (2019) - [i36]Alexander Mey, Marco Loog:
Consistency and Finite Sample Behavior of Binary Class Probability Estimation. CoRR abs/1908.11823 (2019) - [i35]Tom J. Viering, Alexander Mey, Marco Loog:
Making Learners (More) Monotone. CoRR abs/1911.11030 (2019) - 2018
- [j46]Erik J. Bekkers, Marco Loog, Bart M. ter Haar Romeny, Remco Duits:
Template Matching via Densities on the Roto-Translation Group. IEEE Trans. Pattern Anal. Mach. Intell. 40(2): 452-466 (2018) - [j45]Yazhou Yang, Marco Loog:
A variance maximization criterion for active learning. Pattern Recognit. 78: 358-370 (2018) - [j44]Yazhou Yang, Marco Loog:
A benchmark and comparison of active learning for logistic regression. Pattern Recognit. 83: 401-415 (2018) - [c105]Wouter M. Kouw, Marco Loog:
Effects of sampling skewness of the importance-weighted risk estimator on model selection. ICPR 2018: 1468-1473 - [c104]Marijn van Stralen, Y. Zhou, P. J. Wozny, Peter R. Seevinck, Marco Loog:
Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI. Medical Imaging: Image Processing 2018: 105741N - [c103]Jesse H. Krijthe, Marco Loog:
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. NeurIPS 2018: 1795-1804 - [c102]Antonella Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax:
Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning. S+SSPR 2018: 119-129 - [c101]Lorenzo Bottarelli, Marco Loog:
Gradient Descent for Gaussian Processes Variance Reduction. S+SSPR 2018: 160-169 - [i34]Wouter M. Kouw, Marco Loog:
Effects of sampling skewness of the importance-weighted risk estimator on model selection. CoRR abs/1804.07344 (2018) - [i33]Yazhou Yang, Marco Loog:
Single Shot Active Learning using Pseudo Annotators. CoRR abs/1805.06660 (2018) - [i32]Wouter M. Kouw, Marco Loog:
Target Contrastive Pessimistic Discriminant Analysis. CoRR abs/1806.09463 (2018) - [i31]Julius von Kügelgen, Alexander Mey, Marco Loog:
Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. CoRR abs/1807.07879 (2018) - [i30]Lex Razoux Schultz, Marco Loog, Peyman Mohajerin Esfahani:
Distance Based Source Domain Selection for Sentiment Classification. CoRR abs/1808.09271 (2018) - [i29]Wouter M. Kouw, Marco Loog, Wilbert Bartels, Adriënne M. Mendrik:
Learning an MR acquisition-invariant representation using Siamese neural networks. CoRR abs/1810.07430 (2018) - 2017
- [j43]Jesse H. Krijthe, Marco Loog:
Projected estimators for robust semi-supervised classification. Mach. Learn. 106(7): 993-1008 (2017) - [j42]Jesse H. Krijthe, Marco Loog:
Robust semi-supervised least squares classification by implicit constraints. Pattern Recognit. 63: 115-126 (2017) - [j41]Jianxin Wu, Xiang Bai, Marco Loog, Fabio Roli, Zhi-Hua Zhou:
Editorial of the Special Issue on Multi-instance Learning in Pattern Recognition and Vision. Pattern Recognit. 71: 444-445 (2017) - [c100]Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert:
Object-Extent Pooling for Weakly Supervised Single-Shot Localization. BMVC 2017 - [c99]Marco Loog, François Lauze:
Supervised Scale-Regularized Linear Convolutionary Filters. BMVC 2017 - [i28]Yazhou Yang, Marco Loog:
Active Learning Using Uncertainty Information. CoRR abs/1702.08540 (2017) - [i27]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Holst Pedersen, Marco Loog, Marleen de Bruijne:
Classification of COPD with Multiple Instance Learning. CoRR abs/1703.04980 (2017) - [i26]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog:
Label Stability in Multiple Instance Learning. CoRR abs/1703.04986 (2017) - [i25]Tom J. Viering, Jesse H. Krijthe, Marco Loog:
Nuclear Discrepancy for Active Learning. CoRR abs/1706.02645 (2017) - [i24]Yazhou Yang, Marco Loog:
A Variance Maximization Criterion for Active Learning. CoRR abs/1706.07642 (2017) - [i23]Wouter M. Kouw, Marco Loog:
Target contrastive pessimistic risk for robust domain adaptation. CoRR abs/1706.08082 (2017) - [i22]Marco Loog, François Lauze:
Scale-Regularized Filter Learning. CoRR abs/1707.02813 (2017) - [i21]Marco Loog, Jesse H. Krijthe, Are Charles Jensen:
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL. CoRR abs/1707.04025 (2017) - [i20]Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert:
Object-Extent Pooling for Weakly Supervised Single-Shot Localization. CoRR abs/1707.06180 (2017) - [i19]Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik:
MR Acquisition-Invariant Representation Learning. CoRR abs/1709.07944 (2017) - [i18]Wouter M. Kouw, Marco Loog:
On reducing sampling variance in covariate shift using control variates. CoRR abs/1710.06514 (2017) - [i17]Marco Loog:
Supervised Classification: Quite a Brief Overview. CoRR abs/1710.09230 (2017) - 2016
- [j40]Wouter M. Kouw, Laurens J. P. van der Maaten, Jesse H. Krijthe, Marco Loog:
Feature-Level Domain Adaptation. J. Mach. Learn. Res. 17: 171:1-171:32 (2016) - [j39]Marco Loog:
Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(3): 462-475 (2016) - [j38]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Dissimilarity-Based Ensembles for Multiple Instance Learning. IEEE Trans. Neural Networks Learn. Syst. 27(6): 1379-1391 (2016) - [c98]Yenisel Plasencia Calana, Yan Li, Robert P. W. Duin, Mauricio Orozco-Alzate, Marco Loog, Edel B. García Reyes:
A Compact Representation of Multiscale Dissimilarity Data by Prototype Selection. CIARP 2016: 150-157 - [c97]Yuan Zeng, Jiexiong Tang, Jan C. A. van der Lubbe, Marco Loog:
Learning Algorithms for Digital Reconstruction of Van Gogh's Drawings. EuroMed (1) 2016: 322-333 - [c96]Jesse H. Krijthe, Marco Loog:
Reproducible Pattern Recognition Research: The Case of Optimistic SSL. RRPR@ICPR 2016: 48-59 - [c95]Marco Loog, Yazhou Yang:
An empirical investigation into the inconsistency of sequential active learning. ICPR 2016: 210-215 - [c94]Wouter M. Kouw, Marco Loog:
On regularization parameter estimation under covariate shift. ICPR 2016: 426-431 - [c93]Manuele Bicego, Marco Loog:
Weighted K-Nearest Neighbor revisited. ICPR 2016: 1642-1647 - [c92]Jesse H. Krijthe, Marco Loog:
Optimistic semi-supervised least squares classification. ICPR 2016: 1677-1682 - [c91]Alexander Mey, Marco Loog:
A soft-labeled self-training approach. ICPR 2016: 2604-2609 - [c90]Yazhou Yang, Marco Loog:
Active learning using uncertainty information. ICPR 2016: 2646-2651 - [c89]Jesse H. Krijthe, Marco Loog:
The Peaking Phenomenon in Semi-supervised Learning. S+SSPR 2016: 299-309 - [p2]Marco Loog, Jesse H. Krijthe, Are Charles Jensen:
On Measuring and Quantifying Performance: error rates, surrogate Loss, and an Example in Semi-Supervised Learning. Handbook of Pattern Recognition and Computer Vision 2016: 53-68 - [e9]Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew P. Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise:
Deep Learning and Data Labeling for Medical Applications - First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. Lecture Notes in Computer Science 10008, 2016, ISBN 978-3-319-46975-1 [contents] - [e8]Antonio Robles-Kelly, Marco Loog, Battista Biggio, Francisco Escolano, Richard C. Wilson:
Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2016, Mérida, Mexico, November 29 - December 2, 2016, Proceedings. Lecture Notes in Computer Science 10029, 2016, ISBN 978-3-319-49054-0 [contents] - [i16]Jesse H. Krijthe, Marco Loog:
Projected Estimators for Robust Semi-supervised Classification. CoRR abs/1602.07865 (2016) - [i15]Erik J. Bekkers, Marco Loog, Bart M. ter Haar Romeny, Remco Duits:
Template Matching on the Roto-Translation Group. CoRR abs/1603.03304 (2016) - [i14]Wouter M. Kouw, Marco Loog:
On Regularization Parameter Estimation under Covariate Shift. CoRR abs/1608.00250 (2016) - [i13]Jesse H. Krijthe, Marco Loog:
Optimistic Semi-supervised Least Squares Classification. CoRR abs/1610.03713 (2016) - [i12]Jesse H. Krijthe, Marco Loog:
The Peaking Phenomenon in Semi-supervised Learning. CoRR abs/1610.05160 (2016) - [i11]Jesse H. Krijthe, Marco Loog:
Reproducible Pattern Recognition Research: The Case of Optimistic SSL. CoRR abs/1612.08650 (2016) - [i10]Jesse H. Krijthe, Marco Loog:
The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning. CoRR abs/1612.08875 (2016) - 2015
- [j37]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Multiple instance learning with bag dissimilarities. Pattern Recognit. 48(1): 264-275 (2015) - [j36]Ethem Alpaydin, Veronika Cheplygina, Marco Loog, David M. J. Tax:
Single- vs. multiple-instance classification. Pattern Recognit. 48(9): 2831-2838 (2015) - [j35]Veronika Cheplygina, David M. J. Tax, Marco Loog:
On classification with bags, groups and sets. Pattern Recognit. Lett. 59: 11-17 (2015) - [j34]Marco Loog, Are Charles Jensen:
Semi-Supervised Nearest Mean Classification Through a Constrained Log-Likelihood. IEEE Trans. Neural Networks Learn. Syst. 26(5): 995-1006 (2015) - [c88]Jesse H. Krijthe, Marco Loog:
Implicitly Constrained Semi-supervised Least Squares Classification. IDA 2015: 158-169 - [c87]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog:
Label Stability in Multiple Instance Learning. MICCAI (1) 2015: 539-546 - [e7]Aasa Feragen, Marcello Pelillo, Marco Loog:
Similarity-Based Pattern Recognition - Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015, Proceedings. Lecture Notes in Computer Science 9370, Springer 2015, ISBN 978-3-319-24260-6 [contents] - [i9]Marco Loog:
Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. CoRR abs/1503.00269 (2015) - [i8]Jesse H. Krijthe, Marco Loog:
Implicitly Constrained Semi-Supervised Least Squares Classification. CoRR abs/1507.06802 (2015) - [i7]Wouter M. Kouw, Jesse H. Krijthe, Marco Loog, Laurens J. P. van der Maaten:
Feature-Level Domain Adaptation. CoRR abs/1512.04829 (2015) - [i6]Jesse H. Krijthe, Marco Loog:
Robust Semi-supervised Least Squares Classification by Implicit Constraints. CoRR abs/1512.08240 (2015) - 2014
- [j33]Menno A. Smeelen, Piet B. W. Schwering, Alexander Toet, Marco Loog:
Semi-hidden target recognition in gated viewer images fused with thermal IR images. Inf. Fusion 18: 131-147 (2014) - [j32]Marco Loog:
Semi-supervised linear discriminant analysis through moment-constraint parameter estimation. Pattern Recognit. Lett. 37: 24-31 (2014) - [c86]Erik J. Bekkers, Remco Duits, Marco Loog:
Training of Templates for Object Recognition in Invertible Orientation Scores: Application to Optic Nerve Head Detection in Retinal Images. EMMCVPR 2014: 464-477 - [c85]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Johannes Holst Pedersen, Marco Loog, Marleen de Bruijne:
Classification of COPD with Multiple Instance Learning. ICPR 2014: 1508-1513 - [c84]Jesse H. Krijthe, Marco Loog:
Implicitly Constrained Semi-supervised Linear Discriminant Analysis. ICPR 2014: 3762-3767 - [c83]Veronika Cheplygina, David M. J. Tax, Marco Loog, Aasa Feragen:
Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder. MLMI 2014: 190-197 - [c82]Robert P. W. Duin, Manuele Bicego, Mauricio Orozco-Alzate, Sang-Woon Kim, Marco Loog:
Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance. S+SSPR 2014: 183-192 - [e6]Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo:
Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings. Lecture Notes in Computer Science 8621, Springer 2014, ISBN 978-3-662-44414-6 [contents] - [i5]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Dissimilarity-based Ensembles for Multiple Instance Learning. CoRR abs/1402.1349 (2014) - [i4]David M. J. Tax, Veronika Cheplygina, Marco Loog:
Quantile Representation for Indirect Immunofluorescence Image Classification. CoRR abs/1402.1371 (2014) - [i3]Veronika Cheplygina, David M. J. Tax, Marco Loog:
On Classification with Bags, Groups and Sets. CoRR abs/1406.0281 (2014) - [i2]Jesse H. Krijthe, Marco Loog:
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis. CoRR abs/1411.4521 (2014) - 2013
- [j31]Adrien Bartoli, Daniel Pizarro, Marco Loog:
Stratified Generalized Procrustes Analysis. Int. J. Comput. Vis. 101(2): 227-253 (2013) - [j30]Yan Li, David M. J. Tax, Robert P. W. Duin, Marco Loog:
Multiple-instance learning as a classifier combining problem. Pattern Recognit. 46(3): 865-874 (2013) - [j29]Viet Cuong Dinh, Robert P. W. Duin, Ignacio Piqueras-Salazar, Marco Loog:
FIDOS: A generalized Fisher based feature extraction method for domain shift. Pattern Recognit. 46(9): 2510-2518 (2013) - [j28]Raimund Leitner, Martin De Biasio, Thomas Arnold, Viet Cuong Dinh, Marco Loog, Robert P. W. Duin:
Multi-spectral video endoscopy system for the detection of cancerous tissue. Pattern Recognit. Lett. 34(1): 85-93 (2013) - [c81]Adhish Prasoon, Christian Igel, Marco Loog, François Lauze, Erik B. Dam, Mads Nielsen:
Femoral cartilage segmentation in Knee MRI scans using two stage voxel classification. EMBC 2013: 5469-5472 - [c80]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Combining Instance Information to Classify Bags. MCS 2013: 13-24 - [c79]Yan Li, David M. J. Tax, Robert P. W. Duin, Marco Loog:
The Link between Multiple-Instance Learning and Learning from Only Positive and Unlabelled Examples. MCS 2013: 157-166 - [c78]Thies Gehrmann, Marco Loog, Marcel J. T. Reinders, Dick de Ridder:
Conditional Random Fields for Protein Function Prediction. PRIB 2013: 184-195 - [c77]Yenisel Plasencia Calana, Veronika Cheplygina, Robert P. W. Duin, Edel B. García Reyes, Mauricio Orozco-Alzate, David M. J. Tax, Marco Loog:
On the Informativeness of Asymmetric Dissimilarities. SIMBAD 2013: 75-89 - [p1]Robert P. W. Duin, Elzbieta Pekalska, Marco Loog:
Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness. Similarity-Based Pattern Analysis and Recognition 2013: 13-44 - [i1]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Multiple Instance Learning with Bag Dissimilarities. CoRR abs/1309.5643 (2013) - 2012
- [j27]Wan-Jui Lee, Veronika Cheplygina, David M. J. Tax, Marco Loog, Robert P. W. Duin:
Bridging Structure and Feature Representations in Graph Matching. Int. J. Pattern Recognit. Artif. Intell. 26(5) (2012) - [j26]Yan Li, David M. J. Tax, Marco Loog:
Scale selection for supervised image segmentation. Image Vis. Comput. 30(12): 991-1003 (2012) - [c76]Habil Kalkan, Marius Nap, Robert P. W. Duin, Marco Loog:
Automated classification of local patches in colon histopathology. ICPR 2012: 61-64 - [c75]Viet Cuong Dinh, Marco Loog, Raimund Leitner, Olga Rajadell, Robert P. W. Duin:
Training data selection for cancer detection in multispectral endoscopy images. ICPR 2012: 161-164 - [c74]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Does one rotten apple spoil the whole barrel? ICPR 2012: 1156-1159 - [c73]Konstantin Chernoff, Marco Loog, Mads Nielsen:
Metric learning by directly minimizing the k-NN training error. ICPR 2012: 1265-1268 - [c72]Yan Li, Marco Loog:
Scale-invariant sampling for supervised image segmentation. ICPR 2012: 1399-1402 - [c71]Yan Li, Robert P. W. Duin, Marco Loog:
Combining multi-scale dissimilarities for image classification. ICPR 2012: 1639-1642 - [c70]Viet Cuong Dinh, Robert P. W. Duin, Marco Loog:
A study on semi-supervised dissimilarity representation. ICPR 2012: 2861-2864 - [c69]Jesse H. Krijthe, Tin Kam Ho, Marco Loog:
Improving cross-validation based classifier selection using meta-learning. ICPR 2012: 2873-2876 - [c68]C. Chen, Lauge Sørensen, François Lauze, Christian Igel, Marco Loog, Aasa Feragen, Marleen de Bruijne, Mads Nielsen:
Towards exaggerated emphysema stereotypes. Medical Imaging: Computer-Aided Diagnosis 2012: 83150Q - [c67]Habil Kalkan, Marius Nap, Robert P. W. Duin, Marco Loog:
Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology. MICCAI (3) 2012: 550-557 - [c66]Adhish Prasoon, Christian Igel, Marco Loog, François Lauze, Erik Dam, Mads Nielsen:
Cascaded classifier for large-scale data applied to automatic segmentation of articular cartilage. Medical Imaging: Image Processing 2012: 83144V - [c65]Marco Loog:
Nearest neighbor-based importance weighting. MLSP 2012: 1-6 - [c64]Alessandro Ibba, Robert P. W. Duin, Marco Loog:
Supervised localization of cell nuclei on TMA images. MMBIA 2012: 4321-4327 - [c63]Robert P. W. Duin, Ana L. N. Fred, Marco Loog, Elzbieta Pekalska:
Mode Seeking Clustering by KNN and Mean Shift Evaluated. SSPR/SPR 2012: 51-59 - [c62]Marco Loog, Robert P. W. Duin:
The Dipping Phenomenon. SSPR/SPR 2012: 310-317 - [c61]Marco Loog, Are Charles Jensen:
Constrained Log-Likelihood-Based Semi-supervised Linear Discriminant Analysis. SSPR/SPR 2012: 327-335 - [c60]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Class-Dependent Dissimilarity Measures for Multiple Instance Learning. SSPR/SPR 2012: 602-610 - 2011
- [j25]Aydin Ulas, Robert P. W. Duin, Umberto Castellani, Marco Loog, Pasquale Mirtuono, Manuele Bicego, Vittorio Murino, Marcella Bellani, Stefania Cerruti, Michele Tansella, Paolo Brambilla:
Dissimilarity-based detection of schizophrenia. Int. J. Imaging Syst. Technol. 21(2): 179-192 (2011) - [j24]Viet Cuong Dinh, Raimund Leitner, Pavel Paclík, Marco Loog, Robert P. W. Duin:
SEDMI: Saliency based edge detection in multispectral images. Image Vis. Comput. 29(8): 546-556 (2011) - [j23]Meindert Niemeijer, Marco Loog, Michael D. Abràmoff, Max A. Viergever, Mathias Prokop, Bram van Ginneken:
On Combining Computer-Aided Detection Systems. IEEE Trans. Medical Imaging 30(2): 215-223 (2011) - [c59]Chen Chen, François Lauze, Christian Igel, Aasa Feragen, Marco Loog, Mads Nielsen:
Towards exaggerated image stereotypes. ACPR 2011: 422-426 - [c58]Marco Loog:
Information theoretic preattentive saliency: A closed-form solution. ICCV Workshops 2011: 1418-1424 - [c57]Wan-Jui Lee, Robert P. W. Duin, Marco Loog:
Generalized Augmentation of Multiple Kernels. MCS 2011: 116-125 - [c56]Marco Loog:
Semi-supervised Linear Discriminant Analysis Using Moment Constraints. PSL 2011: 32-41 - [c55]Yan Li, David M. J. Tax, Marco Loog:
Supervised Scale-Invariant Segmentation (and Detection). SSVM 2011: 350-361 - [c54]Are Charles Jensen, Marco Loog:
Forming Different-Complexity Covariance-Model Subspaces through Piecewise-Constant Spectra for Hyperspectral Image Classification. SCIA 2011: 186-195 - [c53]David M. J. Tax, Marco Loog, Robert P. W. Duin, Veronika Cheplygina, Wan-Jui Lee:
Bag Dissimilarities for Multiple Instance Learning. SIMBAD 2011: 222-234 - [c52]Babak Loni, Gijs van Tulder, Pascal Wiggers, David M. J. Tax, Marco Loog:
Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features. TSD 2011: 243-250 - [e5]Marco Loog, Lodewyk F. A. Wessels, Marcel J. T. Reinders, Dick de Ridder:
Pattern Recognition in Bioinformatics - 6th IAPR International Conference, PRIB 2011, Delft, The Netherlands, November 2-4, 2011. Proceedings. Lecture Notes in Computer Science 7036, Springer 2011, ISBN 978-3-642-24854-2 [contents] - 2010
- [j22]Arish A. Qazi, Dan R. Jørgensen, Martin Lillholm, Marco Loog, Mads Nielsen, Erik B. Dam:
A framework for optimizing measurement weight maps to minimize the required sample size. Medical Image Anal. 14(3): 255-264 (2010) - [j21]Marco Loog, François Lauze:
The Improbability of Harris Interest Points. IEEE Trans. Pattern Anal. Mach. Intell. 32(6): 1141-1147 (2010) - [j20]Are Charles Jensen, Marco Loog, Anne H. Schistad Solberg:
Using Multiscale Spectra in Regularizing Covariance Matrices for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote. Sens. 48(4-1): 1851-1859 (2010) - [j19]Ihor Smal, Marco Loog, Wiro J. Niessen, Erik Meijering:
Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy. IEEE Trans. Medical Imaging 29(2): 282-301 (2010) - [c51]Adrien Bartoli, Daniel Pizarro, Marco Loog:
Stratified Generalized Procrustes Analysis. BMVC 2010: 1-10 - [c50]Wan-Jui Lee, Robert P. W. Duin, Alessandro Ibba, Marco Loog:
An experimental study on combining Euclidean distances. CIP 2010: 304-309 - [c49]Robert P. W. Duin, Marco Loog, Elzbieta Pekalska, David M. J. Tax:
Feature-Based Dissimilarity Space Classification. ICPR Contests 2010: 46-55 - [c48]Stefan Klein, Marco Loog, Fedde van der Lijn, Tom den Heijer, Alexander Hammers, Marleen de Bruijne, Aad van der Lugt, Robert P. W. Duin, Monique M. B. Breteler, Wiro J. Niessen:
Early diagnosis of dementia based on intersubject whole-brain dissimilarities. ISBI 2010: 249-252 - [c47]Lauge Sørensen, Marco Loog, Pechin Lo, Haseem Ashraf, Asger Dirksen, Robert P. W. Duin, Marleen de Bruijne:
Image Dissimilarity-Based Quantification of Lung Disease from CT. MICCAI (1) 2010: 37-44 - [c46]Mehrdad J. Gangeh, Lauge Sørensen, Saher B. Shaker, Mohamed S. Kamel, Marleen de Bruijne, Marco Loog:
A Texton-Based Approach for the Classification of Lung Parenchyma in CT Images. MICCAI (3) 2010: 595-602 - [c45]Marco Loog:
Constrained Parameter Estimation for Semi-supervised Learning: The Case of the Nearest Mean Classifier. ECML/PKDD (2) 2010: 291-304 - [c44]Lauge Sørensen, Marco Loog, David M. J. Tax, Wan-Jui Lee, Marleen de Bruijne, Robert P. W. Duin:
Dissimilarity-Based Multiple Instance Learning. SSPR/SPR 2010: 129-138
2000 – 2009
- 2009
- [c43]Melanie Ganz, Marco Loog, Sami S. Brandt, Mads Nielsen:
Dense iterative contextual pixel classification using Kriging. CVPR Workshops 2009: 87-93 - [c42]Stefan Sommer, Aditya Tatu, Chen Chen, D. R. Jurgensen, Marleen de Bruijne, Marco Loog, Mads Nielsen, François Lauze:
Bicycle chain shape models. CVPR Workshops 2009: 157-163 - [c41]Marco Loog, Marleen de Bruijne:
Discriminative Shape Alignment. IPMI 2009: 459-466 - [c40]Ihor Smal, Marco Loog, Wiro J. Niessen, Erik Meijering:
Quantitative Comparison of Spot Detection Methods in Live-Cell Fluorescence Microscopy Imaging. ISBI 2009: 1175-1178 - [c39]David M. J. Tax, Marco Loog, Robert P. W. Duin:
Optimal Mean-Precision Classifier. MCS 2009: 72-81 - [c38]Marco Loog, Yan Li, David M. J. Tax:
Maximum Membership Scale Selection. MCS 2009: 468-477 - 2008
- [j18]Marco Loog, Xiaojun Wu, Jieping Lu, Jing-Yu Yang, Shitong Wang, Josef Kittler:
A note on an extreme case of the generalized optimal discriminant transformation. Neurocomputing 72(1-3): 664-665 (2008) - [j17]Bo Markussen, Kim Steenstrup Pedersen, Marco Loog:
Second Order Structure of Scale-Space Measurements. J. Math. Imaging Vis. 31(2-3): 207-220 (2008) - [j16]Marco Loog:
On Distributional Assumptions and Whitened Cosine Similarities. IEEE Trans. Pattern Anal. Mach. Intell. 30(6): 1114-1115 (2008) - [j15]Jakob Raundahl, Marco Loog, Paola Pettersen, László B. Tankó, Mads Nielsen:
Automated Effect-Specific Mammographic Pattern Measures. IEEE Trans. Medical Imaging 27(8): 1054-1060 (2008) - [j14]Erik B. Dam, Marco Loog:
Efficient Segmentation by Sparse Pixel Classification. IEEE Trans. Medical Imaging 27(10): 1525-1534 (2008) - [c37]Arish A. Qazi, Erik B. Dam, Marco Loog, Mads Nielsen, François Lauze, Claus Christiansen:
A variational method for automatic localization of the most pathological ROI in the knee cartilage. Medical Imaging: Image Processing 2008: 69140T - [e4]Niels da Vitoria Lobo, Takis Kasparis, Fabio Roli, James Tin-Yau Kwok, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Marco Loog:
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. Lecture Notes in Computer Science 5342, Springer 2008, ISBN 978-3-540-89688-3 [contents] - 2007
- [j13]Marco Loog:
A Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion. J. Mach. Learn. Res. 8: 2121-2123 (2007) - [j12]Marco Loog:
On an alternative formulation of the Fisher criterion that overcomes the small sample problem. Pattern Recognit. 40(6): 1753-1755 (2007) - [c36]Marco Loog:
Localized Maximum Entropy Shape Modelling. IPMI 2007: 619-629 - [c35]Juan Eugenio Iglesias, Marleen de Bruijne, Marco Loog, François Lauze, Mads Nielsen:
A Family of Principal Component Analyses for Dealing with Outliers. MICCAI (2) 2007: 178-185 - [c34]Jakob Raundahl, Marco Loog, Paola Pettersen, Mads Nielsen:
Quantifying Effect-Specific Mammographic Density. MICCAI (2) 2007: 580-587 - [c33]Jakob Raundahl, Marco Loog, Paola Pettersen, Mads Nielsen:
Evaluation of four mammographic density measures on HRT data. Medical Imaging: Image Processing 2007: 65121F - [c32]Marco Loog:
The Jet Metric. SSVM 2007: 25-31 - [c31]Kim Steenstrup Pedersen, Marco Loog, Bo Markussen:
Generic Maximum Likely Scale Selection. SSVM 2007: 362-373 - [c30]Marco Loog, François Lauze:
Blur Invariant Image Priors. SSVM 2007: 665-674 - [c29]Kim Steenstrup Pedersen, Marco Loog, Pieter van Dorst:
Salient Point and Scale Detection by Minimum Likelihood. Gaussian Processes in Practice 2007: 59-72 - [e3]De-Shuang Huang, Laurent Heutte, Marco Loog:
Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007. Proceedings. Communications in Computer and Information Science 2, Springer 2007, ISBN 978-3-540-74281-4 [contents] - [e2]De-Shuang Huang, Laurent Heutte, Marco Loog:
Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings. Lecture Notes in Computer Science 4681, Springer 2007, ISBN 978-3-540-74170-1 [contents] - [e1]De-Shuang Huang, Laurent Heutte, Marco Loog:
Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, Proceedings. Lecture Notes in Computer Science 4682, Springer 2007, ISBN 978-3-540-74201-2 [contents] - 2006
- [j11]Bram van Ginneken, Mikkel B. Stegmann, Marco Loog:
Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical Image Anal. 10(1): 19-40 (2006) - [j10]Arnold M. R. Schilham, Bram van Ginneken, Marco Loog:
A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Medical Image Anal. 10(2): 247-258 (2006) - [j9]Marco Loog, Bram van Ginneken, Arnold M. R. Schilham:
Filter learning: Application to suppression of bony structures from chest radiographs. Medical Image Anal. 10(6): 826-840 (2006) - [j8]A. Kai Qin, Ponnuthurai N. Suganthan, Marco Loog:
Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction. Pattern Recognit. 39(9): 1805-1808 (2006) - [j7]Robert P. W. Duin, Marco Loog, Tin Kam Ho:
Recent submissions in linear dimensionality reduction and face recognition. Pattern Recognit. Lett. 27(7): 707-708 (2006) - [j6]Marco Loog, Bram van Ginneken:
Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. IEEE Trans. Medical Imaging 25(5): 602-611 (2006) - [c28]Marco Loog, Bram van Ginneken:
Bony Structure Suppression in Chest Radiographs. CVAMIA 2006: 166-177 - [c27]A. Kai Qin, Ponnuthurai N. Suganthan, Marco Loog:
Efficient Feature Extraction Based on Regularized Uncorrelated Chernoff Discriminant Analysis. ICPR (3) 2006: 125-128 - [c26]Marco Loog, Dick de Ridder:
Local Discriminant Analysis. ICPR (3) 2006: 328-331 - [c25]Marco Loog:
Conditional Linear Discriminant Analysis. ICPR (2) 2006: 387-390 - [c24]Jakob Raundahl, Marco Loog, Mads Nielsen:
Understanding Hessian-Based Density Scoring. Digital Mammography / IWDM 2006: 447-452 - [c23]Jakob Raundahl, Marco Loog, Mads Nielsen:
Mammographic density measured as changes in tissue structure caused by HRT. Medical Imaging: Image Processing 2006: 61440G - [c22]Marco Loog:
Generic Blind Source Separation Using Second-Order Local Statistics. SSPR/SPR 2006: 844-852 - 2005
- [j5]A. Kai Qin, Ponnuthurai N. Suganthan, Marco Loog:
Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion. Pattern Recognit. 38(4): 613-616 (2005) - [j4]Marco Loog, Bram van Ginneken, Robert P. W. Duin:
Dimensionality reduction of image features using the canonical contextual correlation projection. Pattern Recognit. 38(12): 2409-2418 (2005) - [c21]A. Kai Qin, S. Y. M. Shi, Ponnuthurai N. Suganthan, Marco Loog:
Enhanced Direct Linear Discriminant Analysis for Feature Extraction on High Dimensional Data. AAAI 2005: 851-855 - [c20]Bo Markussen, Kim Steenstrup Pedersen, Marco Loog:
A Scale Invariant Covariance Structure on Jet Space. DSSCV 2005: 12-23 - [c19]Marco Loog, Kim Steenstrup Pedersen, Bo Markussen:
Maximum Likely Scale Estimation. DSSCV 2005: 146-156 - 2004
- [b1]Marco Loog:
Supervised dimensionality reduction and contextual pattern recognition in medical image processing. Utrecht University, Netherlands, 2004 - [j3]Marco Loog, Robert P. W. Duin, Max A. Viergever:
The MDF discrimination measure: Fisher in disguise. Neural Networks 17(4): 563-566 (2004) - [j2]Marco Loog, Robert P. W. Duin:
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion. IEEE Trans. Pattern Anal. Mach. Intell. 26(6): 732-739 (2004) - [c18]Marco Loog:
Support Blob Machines. The Sparsification of Linear Scale Space. ECCV (4) 2004: 14-24 - [c17]Marco Loog, Bram van Ginneken, Robert P. W. Duin:
Dimensionality Reduction by Canonical Contextual Correlation Projections. ECCV (1) 2004: 562-573 - [c16]Dick de Ridder, Marco Loog, Marcel J. T. Reinders:
Local Fisher Embedding. ICPR (2) 2004: 295-298 - [c15]Marco Loog, Bram van Ginneken:
Static Posterior Probability Fusion for Signal Detection: Applications in the Detection of Interstitial Diseases in Chest Radiographs. ICPR (1) 2004: 644-647 - [c14]Bram van Ginneken, Marco Loog:
Pixel Position Regression - Application to Medical Image Segmentation. ICPR (3) 2004: 718-721 - [c13]Erik Dam, Marco Loog, Marloes M. J. Letteboer:
Integrating Automatic and Interactive Brain Tumor Segmentation. ICPR (3) 2004: 790-793 - [c12]Meindert Niemeijer, Joes Staal, Bram van Ginneken, Marco Loog, Michael D. Abràmoff:
Comparative study of retinal vessel segmentation methods on a new publicly available database. Medical Imaging: Image Processing 2004 - 2003
- [c11]Arnold M. R. Schilham, Bram van Ginneken, Marco Loog:
Multi-scale Nodule Detection in Chest Radiographs. MICCAI (1) 2003: 602-609 - [c10]Marleen de Bruijne, Bram van Ginneken, Wiro J. Niessen, Marco Loog, Max A. Viergever:
Model-based segmentation of abdominal aortic aneurysms in CTA images. Medical Imaging: Image Processing 2003 - [c9]Bram van Ginneken, Marleen de Bruijne, Marco Loog, Max A. Viergever:
Interactive shape models. Medical Imaging: Image Processing 2003 - [c8]Marco Loog, Bram van Ginneken, Max A. Viergever:
Segmenting the posterior ribs in chest radiographs by iterated contextual pixel classification. Medical Imaging: Image Processing 2003 - [c7]Marco Loog, Martin Lillholm, Mads Nielsen, Max A. Viergever:
Gaussian Scale Space from Insufficient Image Information. Scale-Space 2003: 757-769 - 2002
- [c6]Marco Loog, Bram van Ginneken:
Supervised Segmentation by Iterated Contextual Pixel Classification. ICPR (2) 2002: 925-928 - [c5]Marco Loog, Robert P. W. Duin:
Non-iterative Heteroscedastic Linear Dimension Reduction for Two-Class Data. SSPR/SPR 2002: 508-517 - 2001
- [j1]Marco Loog, Robert P. W. Duin, Reinhold Haeb-Umbach:
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria. IEEE Trans. Pattern Anal. Mach. Intell. 23(7): 762-766 (2001) - [c4]Marco Loog, Johannes Jisse Duistermaat, Luc Florack:
On the Behavior of Spatial Critical Points under Gaussian Blurring. A Folklore Theorem and Scale-Space Constraints. Scale-Space 2001: 183-192 - 2000
- [c3]Robert P. W. Duin, Marco Loog, Reinhold Haeb-Umbach:
Multi-Class Linear Feature Extraction by Nonlinear PCA. ICPR 2000: 2398-2401 - [c2]Marco Loog, Reinhold Haeb-Umbach:
Multi-class linear dimension reduction by generalized Fisher criteria. INTERSPEECH 2000: 1069-1072
1990 – 1999
- 1999
- [c1]Reinhold Haeb-Umbach, Marco Loog:
An investigation of cepstral parameterisations for large vocabulary speech recognition. EUROSPEECH 1999
Coauthor Index
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