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
1672015
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
312016
Implicitly constrained semi-supervised least squares classification
JH Krijthe, M Loog
International symposium on intelligent data analysis, 158-169, 2015
232015
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
202016
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
JH Krijthe, M Loog
Pattern Recognition (ICPR), 2014 22nd International Conference on, 3762-3767, 2014
152014
Robust semi-supervised least squares classification by implicit constraints
JH Krijthe, M Loog
Pattern Recognition 63, 115-126, 2017
132017
Syntactic pattern recognition: paradigm issues and open problems
M Flasiński
Handbook of Pattern Recognition and Computer Vision, 3-25, 2016
132016
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
92017
RSSL: Semi-supervised Learning in R
JH Krijthe
International Workshop on Reproducible Research in Pattern Recognition, 104-115, 2016
82016
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
Optimistic semi-supervised least squares classification
JH Krijthe, M Loog
2016 23rd International Conference on Pattern Recognition (ICPR), 1677-1682, 2016
62016
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
22019
Autoencoding Credit Card Fraud
T Sweers, T Heskes, J Krijthe
22018
Nuclear discrepancy for active learning
TJ Viering, JH Krijthe, M Loog
arXiv preprint arXiv:1706.02645, 2017
12017
Reproducible pattern recognition research: the case of optimistic SSL
JH Krijthe, M Loog
International Workshop on Reproducible Research in Pattern Recognition, 48-59, 2016
12016
The peaking phenomenon in semi-supervised learning
JH Krijthe, M Loog
Joint IAPR International Workshops on Statistical Techniques in Pattern …, 2016
12016
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
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Articles 1–20