This paper proposes a novel Mass Spectrometry data profiling method for ovarian cancer detection based on negative correlation learning (NCL). A modified Smoothed Nonlinear Energy ...
Harmonic analysis and diffusion on discrete data has been shown to lead to state-of-theart algorithms for machine learning tasks, especially in the context of semi-supervised and ...
Arthur D. Szlam, Mauro Maggioni, Ronald R. Coifman
Background: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard super...
The importance of bringing causality into play when designing feature selection methods is more and more acknowledged in the machine learning community. This paper proposes a filt...
In this paper, we compare both discriminative and generative parameter learning on both discriminatively and generatively structured Bayesian network classifiers. We use either ma...