3 papers
stat.ML2016
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…
stat.ML2016
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…
stat.ML2016
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…