A Bayesian network model is a popular technique for data mining due to its intuitive interpretation. This paper presents a semantic genetic algorithm (SGA) to learn a complete qual...
We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can ...
When multiple data sources are available for clustering, an a priori data integration process is usually required. This process may be costly and may not lead to good clusterings,...
Elisa Boari de Lima, Raquel Cardoso de Melo Minard...
Abstract We present a new margin-based approach to first-order rule learning. The approach addresses many of the prominent challenges in first-order rule learning, such as the comp...
We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages ove...