Publications

@article{alaiz-rodriguez_minimax_2005,

title = {Minimax classifiers based on neural networks},

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

url = {https://www.sciencedirect.com/science/article/pii/S0031320304002365},

year  = {2005},

date = {2005-01-01},

journal = {Pattern Recognition},

volume = {38},

number = {1},

pages = {29–39},

abstract = {This paper addresses the challenge of designing classifiers when prior probabilities are unknown or not representative of the underlying data distribution. Traditional methods assume stationary class priors, leading to suboptimal results when there is a mismatch between training priors and real-world priors. To mitigate this issue, a minimax approach is proposed. The paper presents two algorithms for a neural-based minimax classifier: a learning rate scaling algorithm and a gradient-based algorithm. Experimental results demonstrate that both algorithms successfully find the minimax solution. Additionally, the paper highlights the differences between common approaches and the minimax classifier in dealing with prior uncertainty.},

note = {Publisher: Pergamon},

keywords = {Minimax Decision Rules, neural networks, Pattern Classification, Uncertainty in Priors},

pubstate = {published},

tppubtype = {article}

}