| Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation JH Krijthe R package version 0.13, URL https://github. com/jkrijthe/Rtsne, 2015 | 171 | 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 | 33 | 2016 |
| Implicitly constrained semi-supervised least squares classification JH Krijthe, M Loog International symposium on intelligent data analysis, 158-169, 2015 | 24 | 2015 |
| 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 (1), 1-14, 2016 | 22 | 2016 |
| Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation. R package version 0.13 JH Krijthe Computer Software, 2015 | 19 | 2015 |
| Robust semi-supervised least squares classification by implicit constraints JH Krijthe, M Loog Pattern Recognition 63, 115-126, 2017 | 16 | 2017 |
| Implicitly Constrained Semi-Supervised Linear Discriminant Analysis JH Krijthe, M Loog Pattern Recognition (ICPR), 2014 22nd International Conference on, 3762-3767, 2014 | 14 | 2014 |
| Syntactic pattern recognition: paradigm issues and open problems M Flasiński Handbook of pattern recognition and computer vision, 3-25, 2016 | 13 | 2016 |
| RSSL: Semi-supervised Learning in R JH Krijthe International Workshop on Reproducible Research in Pattern Recognition, 104-115, 2016 | 12 | 2016 |
| Projected estimators for robust semi-supervised classification JH Krijthe, M Loog Machine Learning 106 (7), 993-1008, 2017 | 11 | 2017 |
| Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation https://github. com/jkrijthe JH Krijthe Rtsne, 2015 | 10 | 2015 |
| 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 |
| 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 34 (10), 1480-1487, 2019 | 6 | 2019 |
| 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 | 6 | 2018 |
| Optimistic semi-supervised least squares classification JH Krijthe, M Loog 2016 23rd International Conference on Pattern Recognition (ICPR), 1677-1682, 2016 | 6 | 2016 |
| L. T-distributed stochastic neighbor embedding using a barnes-hut implementation J Krijthe, R van der Maaten, L Rtsne | 5 | 2015 |
| Autoencoding Credit Card Fraud T Sweers, T Heskes, J Krijthe Bachelor Thesis, 2018 | 2 | 2018 |
| The peaking phenomenon in semi-supervised learning JH Krijthe, M Loog Joint IAPR International Workshops on Statistical Techniques in Pattern …, 2016 | 2 | 2016 |
| A brief prehistory of double descent M Loog, T Viering, A Mey, JH Krijthe, DMJ Tax Proceedings of the National Academy of Sciences 117 (20), 10625-10626, 2020 | 1 | 2020 |
Marco LoogDelft University of Technology & University of CopenhagenVerified email at tudelft.nl
