Jesse H. Krijthe
Title
Cited by
Cited by
Year
Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation
JH Krijthe
R package version 0.13, URL https://github. com/jkrijthe/Rtsne, 2015
1712015
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
332016
Implicitly constrained semi-supervised least squares classification
JH Krijthe, M Loog
International symposium on intelligent data analysis, 158-169, 2015
242015
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
222016
Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation. R package version 0.13
JH Krijthe
Computer Software, 2015
192015
Robust semi-supervised least squares classification by implicit constraints
JH Krijthe, M Loog
Pattern Recognition 63, 115-126, 2017
162017
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
JH Krijthe, M Loog
Pattern Recognition (ICPR), 2014 22nd International Conference on, 3762-3767, 2014
142014
Syntactic pattern recognition: paradigm issues and open problems
M Flasiński
Handbook of pattern recognition and computer vision, 3-25, 2016
132016
RSSL: Semi-supervised Learning in R
JH Krijthe
International Workshop on Reproducible Research in Pattern Recognition, 104-115, 2016
122016
Projected estimators for robust semi-supervised classification
JH Krijthe, M Loog
Machine Learning 106 (7), 993-1008, 2017
112017
Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation https://github. com/jkrijthe
JH Krijthe
Rtsne, 2015
102015
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
82016
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
82012
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
62019
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
62018
Optimistic semi-supervised least squares classification
JH Krijthe, M Loog
2016 23rd International Conference on Pattern Recognition (ICPR), 1677-1682, 2016
62016
L. T-distributed stochastic neighbor embedding using a barnes-hut implementation
J Krijthe, R van der Maaten, L Rtsne
52015
Autoencoding Credit Card Fraud
T Sweers, T Heskes, J Krijthe
Bachelor Thesis, 2018
22018
The peaking phenomenon in semi-supervised learning
JH Krijthe, M Loog
Joint IAPR International Workshops on Statistical Techniques in Pattern …, 2016
22016
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
12020
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Articles 1–20