@inbook {421,
	title = {Estimation of Posterior Probabilities with Neural Networks: Application to Microcalcification Detection in Breast Cancer Diagnosis},
	booktitle = {Handbook of Neural Engineering},
	year = {2006},
	pages = {41-58},
	publisher = {John Wiley \& Sons, Inc.},
	organization = {John Wiley \& Sons, Inc.},
	isbn = {9780470056691},
	doi = {10.1002/9780470068298.ch3},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-42249107409\&partnerID=40\&md5=aac6237961cec1a48c0e843a9a1912a4},
	author = {J I Arribas and Jes{\'u}s Cid-Sueiro and Carlos Alberola-Lopez}
}
@inbook {arribas2005estimation,
	title = {Estimation of Posterior Probabilities with Neural Networks: Application to Microcalcification Detection in Breast Cancer Diagnosis},
	booktitle = {Handbook of Neural Engineering},
	year = {2005},
	pages = {41{\textendash}58},
	publisher = {Wiley Online Library},
	organization = {Wiley Online Library},
	author = {J I Arribas and Jes{\'u}s Cid-Sueiro and Carlos Alberola-Lopez}
}
@conference {412,
	title = {Estimates of constrained multi-class a posteriori probabilities in time series problems with neural networks},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks},
	year = {1999},
	publisher = {IEEE, United States},
	organization = {IEEE, United States},
	address = {Washington, DC, USA},
	abstract = {<p>In time series problems, where time ordering is a crucial issue, the use of Partial Likelihood Estimation (PLE) represents a specially suitable method for the estimation of parameters in the model. We propose a new general supervised neural network algorithm, Joint Network and Data Density Estimation (JNDDE), that employs PLE to approximate conditional probability density functions for multi-class classification problems. The logistic regression analysis is generalized to multiple class problems with softmax regression neural network used to model the a-posteriori probabilities such that they are approximated by the network outputs. Constraints to the network architecture, as well as to the model of data, are imposed, resulting in both a flexible network architecture and distribution modeling. We consider application of JNDDE to channel equalization and present simulation results.</p>
},
	keywords = {Approximation theory, Computer simulation, Constraint theory, Data structures, Joint network-data density estimation (JNDDE), Mathematical models, Multi-class a posteriori probabilities, Neural networks, Partial likelihood estimation (PLE), Probability density function, Regression analysis},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0033325263\&partnerID=40\&md5=8c6134020b0b2a9c5ab05b131c070b88},
	author = {J I Arribas and Jes{\'u}s Cid-Sueiro and T Adali and H Ni and B Wang and A R Figueiras-Vidal}
}
