Abstract
rFBP: Replicated Focusing Belief Propagation algorithm
Highlights
The rFBP project implements a scikit-learn compatible machine-learning binary classifier leveraging fully connected neural networks with a learning algorithm (Replicated Focusing Belief Propagation, rFBP) that is quickly converging and robust for ill-posed datasets
In this project we show one of these algorithms developed by Baldassi et al (Baldassi et al, 2016a) and called Replicated Focusing Belief Propagation
The model is based on a spin-glass distribution of neurons put on a fully connected neural network architecture
Summary
The rFBP project implements a scikit-learn compatible machine-learning binary classifier leveraging fully connected neural networks with a learning algorithm (Replicated Focusing Belief Propagation, rFBP) that is quickly converging and robust (less prone to brittle overfitting) for ill-posed datasets (very few samples compared to the number of features). This library has already been widely used to successfully predict source attribution starting from GWAS (Genome Wide Association Studies) data. Classification of Genome Wide Association data by Belief Propagation Neural network, CCS Italy 2019, Conference paper
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