| Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation JH Krijthe R package version 0.13, URL https://github. com/jkrijthe/Rtsne, 2015 | 167 | 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 | 31 | 2016 |
| Implicitly constrained semi-supervised least squares classification JH Krijthe, M Loog International symposium on intelligent data analysis, 158-169, 2015 | 23 | 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 | 20 | 2016 |
| Implicitly Constrained Semi-Supervised Linear Discriminant Analysis JH Krijthe, M Loog Pattern Recognition (ICPR), 2014 22nd International Conference on, 3762-3767, 2014 | 15 | 2014 |
| Robust semi-supervised least squares classification by implicit constraints JH Krijthe, M Loog Pattern Recognition 63, 115-126, 2017 | 13 | 2017 |
| Syntactic pattern recognition: paradigm issues and open problems M Flasiński Handbook of Pattern Recognition and Computer Vision, 3-25, 2016 | 13 | 2016 |
| Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. 2015 https://github. com/jkrijthe K JH Rtsne, 0 | 10 | |
| Projected estimators for robust semi-supervised classification JH Krijthe, M Loog Machine Learning 106 (7), 993-1008, 2017 | 9 | 2017 |
| RSSL: Semi-supervised Learning in R JH Krijthe International Workshop on Reproducible Research in Pattern Recognition, 104-115, 2016 | 8 | 2016 |
| 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 |
| The pessimistic limits of margin-based losses in semi-supervised learning JH Krijthe, M Loog arXiv preprint arXiv:1612.08875, 2016 | 5* | 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 34 (10), 1480-1487, 2019 | 2 | 2019 |
| Autoencoding Credit Card Fraud T Sweers, T Heskes, J Krijthe | 2 | 2018 |
| Nuclear discrepancy for active learning TJ Viering, JH Krijthe, M Loog arXiv preprint arXiv:1706.02645, 2017 | 1 | 2017 |
| 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 |
| Robust importance-weighted cross-validation under sample selection bias WM Kouw, JH Krijthe, M Loog 2019 IEEE 29th International Workshop on Machine Learning for Signal …, 2019 | | 2019 |
Marco LoogDelft University of Technology & University of CopenhagenVerified email at tudelft.nl
