| Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation JH Krijthe R package version 0.13, URL https://github. com/jkrijthe/Rtsne, 2015 | 112* | 2015 |
| Implicitly constrained semi-supervised least squares classification JH Krijthe, M Loog International symposium on intelligent data analysis, 158-169, 2015 | 21 | 2015 |
| Feature-level domain adaptation WM Kouw, LJP Van Der Maaten, JH Krijthe, M Loog The Journal of Machine Learning Research 17 (1), 5943-5974, 2016 | 20 | 2016 |
| Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics E Taskesen, SMH Huisman, A Mahfouz, JH Krijthe, J De Ridder, ... Scientific reports 6, 24949, 2016 | 14 | 2016 |
| Implicitly Constrained Semi-Supervised Linear Discriminant Analysis JH Krijthe, M Loog Pattern Recognition (ICPR), 2014 22nd International Conference on, 3762-3767, 2014 | 13 | 2014 |
| Syntactic pattern recognition: paradigm issues and open problems M Flasiński Handbook of Pattern Recognition and Computer Vision, 3-25, 2016 | 12 | 2016 |
| Robust semi-supervised least squares classification by implicit constraints JH Krijthe, M Loog Pattern Recognition 63, 115-126, 2017 | 11 | 2017 |
| Projected estimators for robust semi-supervised classification JH Krijthe, M Loog Machine Learning 106 (7), 993-1008, 2017 | 9 | 2017 |
| ON MEASURING AND QUANTIFYING PERFORMANCE: ERROR RATES, SURROGATE LOSS, AND AN EXAMPLE IN SEMI-SUPERVISED LEARNING M Loog, JH Krijthe, AC Jensen Handbook of Pattern Recognition and Computer Vision, 53-68, 2016 | 8 | 2016 |
| Improving cross-validation based classifier selection using meta-learning JH Krijthe, TK Ho, M Loog Proceedings of the 21st International Conference on Pattern Recognition …, 2012 | 8 | 2012 |
| Optimistic semi-supervised least squares classification JH Krijthe, M Loog 2016 23rd International Conference on Pattern Recognition (ICPR), 1677-1682, 2016 | 6 | 2016 |
| RSSL: Semi-supervised Learning in R JH Krijthe International Workshop on Reproducible Research in Pattern Recognition, 104-115, 2016 | 5 | 2016 |
| Rtsne: t-distributed stochastic neighbor embedding using Barnes-Hut implementation https://github. com/jkrijthe J Krijthe Rtsne, 2015 | 5 | 2015 |
| The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning JH Krijthe, M Loog arXiv preprint arXiv:1612.08875, 2016 | 2 | 2016 |
| The pessimistic limits and possibilities of margin-based losses in semi-supervised learning J Krijthe, M Loog Advances in Neural Information Processing Systems, 1790-1799, 2018 | 1 | 2018 |
| Reproducible Pattern Recognition Research: The Case of Optimistic SSL JH Krijthe, M Loog International Workshop on Reproducible Research in Pattern Recognition, 48-59, 2016 | 1 | 2016 |
| The peaking phenomenon in semi-supervised learning JH Krijthe, M Loog Joint IAPR International Workshops on Statistical Techniques in Pattern …, 2016 | 1 | 2016 |
| Measuring Parkinson's disease over time: The real‐world within‐subject reliability of the MDS‐UPDRS LJW Evers, JH Krijthe, MJ Meinders, BR Bloem, TM Heskes Movement Disorders, 2019 | | 2019 |
| Predicting Pelvic Floor Surgery Outcomes T Welten, T Heskes, J Krijthe | | 2019 |
| Sex-Specific Patient Journeys in Early Parkinson's Disease in the Netherlands FP Vlaanderen, Y de Man, JH Krijthe, MAC Tanke, AS Groenewoud, ... Frontiers in Neurology 10, 2019 | | 2019 |
Marco LoogDelft University of Technology & University of CopenhagenVerified email at tudelft.nl