Stop marginalizing rare syndromes

Marginalizing Corrupted Features

The goal of machine learning is to develop predictors that generalize well to test data. Ideally, this is achieved by training on an almost infinitely large training data set that captures all variations in the data distribution. In practical learning settings, however, we do not have infinite data and our predictors may overfit. Overfitting may be combatted, for example, by adding a regularize...

MAGSAC: marginalizing sample consensus

A method called σ-consensus is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC. Instead of estimating σ, it is marginalized over a range of noise scales using a Bayesian estimator, i.e. the optimized model is obtained as the weighted average using the posterior probabilities as weights. Applying σ-consensus, two methods are proposed: (i) a postprocessing ste...

Rare Syndromes Associated with Infertility

Marginalizing stacked linear denoising autoencoders

Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. They have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we prop...

Marginalizing in Undirected Graph and Hypergraph Models

Given an undirected graph g or hypergraph 'H model for a given set of variables V , we introduce two marginalization operators for obtaining the undirected graph YA or hy­ pergraph 'HA associated with a given subset A c V such that the marginal distribution of A factorizes according to YA or 'HA, respec­ tively. Finally, we illustrate the method by its application to some practical examples. Wi...