Publications

@article{alaiz-rodriguez_improving_2009,

title = {Improving classification under changes in class and within-class distributions},

author = {Rocío Alaiz-Rodríguez and Alicia Guerrero-Curieses and Jesús Cid-Sueiro},

url = {https://link.springer.com/chapter/10.1007/978-3-642-02478-8_16},

year  = {2009},

date = {2009-01-01},

journal = {International Work-Conference on Artificial Neural Networks},

pages = {122–130},

abstract = {This paper introduces a re-estimation algorithm that adapts classifiers to changing data distributions by using unlabeled operational data. It assumes that classes consist of unknown subclasses and that subclass probabilities may change after training. The method improves performance in scenarios where subclass probabilities change, while maintaining similar results when they don’t.},

note = {Publisher: Springer Berlin Heidelberg Berlin, Heidelberg},

keywords = {Classifier Adaptation, data distribution, Subclass Probabilities},

pubstate = {published},

tppubtype = {article}

}