14 citations · 50 across the 20 of their papers we have counts for
10 papers · 1 filter
Reproducible Pattern Recognition Research: The Case of Optimistic SSL
Jesse H. Krijthe, Marco Loog
In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible. We discuss our…
RSSL: Semi-supervised Learning in R
Jesse H. Krijthe
In this paper, we introduce a package for semi-supervised learning research in the R programming language called RSSL. We cover the purpose of the package, the methods it includes…
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Jesse H. Krijthe, Marco Loog
Consider a classification problem where we have both labeled and unlabeled data available. We show that for linear classifiers defined by convex margin-based surrogate losses that…
The Peaking Phenomenon in Semi-supervised Learning
Jesse H. Krijthe, Marco Loog
For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first incr…
Optimistic Semi-supervised Least Squares Classification
Jesse H. Krijthe, Marco Loog
The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to…
Projected Estimators for Robust Semi-supervised Classification
Jesse H. Krijthe, Marco Loog
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification usin…
Robust Semi-supervised Least Squares Classification by Implicit Constraints
Jesse H. Krijthe, Marco Loog
We introduce the implicitly constrained least squares (ICLS) classifier, a novel semi-supervised version of the least squares classifier. This classifier minimizes the squared loss…
Feature-Level Domain Adaptation
Wouter M. Kouw, Jesse H. Krijthe, Marco Loog +1
Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, wh…
Implicitly Constrained Semi-Supervised Least Squares Classification
Jesse H. Krijthe, Marco Loog
We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled…
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
Jesse H. Krijthe, Marco Loog
Semi-supervised learning is an important and active topic of research in pattern recognition. For classification using linear discriminant analysis specifically, several semi-super…