We propose an algorithm that uses unlabeled test data to adapt the classifier outputs to new operating conditions, without re-training it.
The algorithm is based on a posterior probability model with two main assumptions: (1) the classes may be decomposed in several (unknown) subclasses, and (2) ...
Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift. by Jesus Cid-sueiro. 2011, Neurocomputing. See Full PDF
To cope with this problem, we propose an algorithm that uses unlabeled test data to adapt the classifier outputs to new operating conditions, without re- ...
2019. Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift. R Alaíz-Rodríguez, A Guerrero-Curieses, J Cid ...
Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift. Alaiz-Rodríguez, R. Guerrero-Curieses, A. Cid-Sueiro ...
Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift · Minimax classifiers based on neural networks · Cost- ...
May 26, 2023 · We reveal the general operation process of concept drift adaptive methods under deep learning frameworks and explain concept drift detection ...
Alaiz-Rodríguez, Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift, Neurocomputing, № 74, с. 2614 https ...
Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift. Neurocomputing, Vol. 74, 16 (2011), 2614--2623. https ...