| Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation JH Krijthe R package version 0.13, URL https://github. com/jkrijthe/Rtsne, 2015 | 441* | 2015 |
| 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 | 80 | 2019 |
| 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 | 61 | 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 | 41 | 2020 |
| Real-life gait performance as a digital biomarker for motor fluctuations: the Parkinson@ Home validation study LJW Evers, YP Raykov, JH Krijthe, AL Silva de Lima, R Badawy, K Claes, ... Journal of medical Internet research 22 (10), e19068, 2020 | 33 | 2020 |
| 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), 24949, 2016 | 31 | 2016 |
| Implicitly constrained semi-supervised least squares classification JH Krijthe, M Loog Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015 | 25 | 2015 |
| RSSL: Semi-supervised Learning in R JH Krijthe Reproducible Research in Pattern Recognition: First International Workshop …, 2017 | 21 | 2017 |
| Implicitly Constrained Semi-Supervised Linear Discriminant Analysis JH Krijthe, M Loog Pattern Recognition (ICPR), 2014 22nd International Conference on, 3762-3767, 2014 | 20 | 2014 |
| Projected estimators for robust semi-supervised classification JH Krijthe, M Loog Machine Learning 106, 993-1008, 2017 | 18 | 2017 |
| Robust semi-supervised least squares classification by implicit constraints JH Krijthe, M Loog Pattern Recognition 63, 115-126, 2017 | 15 | 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 | 13 | 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 | 12 | 2012 |
| Autoencoding Credit Card Fraud T Sweers, T Heskes, J Krijthe Bachelor Thesis, 2018 | 11 | 2018 |
| ReproducedPapers. org: Openly teaching and structuring machine learning reproducibility B Yildiz, H Hung, JH Krijthe, CCS Liem, M Loog, G Migut, FA Oliehoek, ... Reproducible Research in Pattern Recognition: Third International Workshop …, 2021 | 8 | 2021 |
| Possible modification of BRSK1 on the risk of alkylating chemotherapy-related reduced ovarian function ALLF Van der Kooi, M Van Dijk, L Broer, MH Van Den Berg, JSE Laven, ... Human Reproduction 36 (4), 1120-1133, 2021 | 8 | 2021 |
| Optimistic semi-supervised least squares classification JH Krijthe, M Loog 2016 23rd International Conference on Pattern Recognition (ICPR), 1677-1682, 2016 | 7 | 2016 |
| Effect of genetic variation in CYP450 on gonadal impairment in a European cohort of female childhood cancer survivors, based on a candidate gene approach: results from the … MEM Van Der Perk, L Broer, Y Yasui, LL Robison, MM Hudson, ... Cancers 13 (18), 4598, 2021 | 6 | 2021 |
| 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, 794, 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 31, 2018 | 6 | 2018 |
Marco LoogRadboud UniversityVerified email at ru.nl
