Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO

Learning Bayesian Networks from Incomplete Databases

Bayesian approaches to learn the graphical structure of Bayesian Belief Networks (BBNs) from databases share the assumption that the database is complete, that is, no entry is re­ ported as unknown. Attempts to relax this assumption involve the use of expensive it­ erative methods to discriminate among dif­ ferent structures. This paper introduces a deterministic method to learn the graphical s...

Learning Bayesian Networks for Regression from Incomplete Databases

In this paper we address the problem of inducing Bayesian network models for regression from incomplete databases. We use mixtures of truncated exponentials (MTEs) to represent the joint distribution in the induced networks. We consider two particular Bayesian network structures, the so-called näıve Bayes and TAN, which have been successfully used as regression models when learning from complet...

Learning conditional probabilities from incomplete databases - An experimental comparison

Learning from Skewed Class Multi-relational Databases

Relational databases, with vast amounts of data–from financial transactions, marketing surveys, medical records, to health informatics observations– and complex schemas, are ubiquitous in our society. Multirelational classification algorithms have been proposed to learn from such relational repositories, where multiple interconnected tables (relations) are involved. These methods search for rel...

Multi-model inference of network properties from incomplete data

It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows ...