Learning Terminological Bayesian Classifiers - A Comparison of Alternative Approaches to Dealing with Unknown Concept-Memberships

The two natural conjugate priors for the Multi variate normal that updates both the mean and the covariance are the normal-inverse- Wishart (if we want to update the mean and

For example, in order to support a model output from the MTS of the Figure 1, the explanations could tell the user that the following itemsets are asso- ciated with the

In this contribution we renew the previous work with Terminesp [5] in order to (1) detect errors and inconsistencies in the data before linking the data set to other lexical

From the table it is clear that sometimes our method gives more precise estimates, but for other values of p 1 , p 2 , Wu’s method will yield more precise results.. However, in

Finally, our Theorem 4 leads to a PAC-Bayesian bound based on both the empirical source error of the Gibbs classifier and the empirical domain disagreement pseu- dometric estimated on

function f (thin line); n=10 evaluations (squares) as obtained by the proposed algorithm using m = 800 for the Monte Carlo integration and Q = 20 for the tagged partition;

However, I have opted for a combined taxonomic-frames approach involving a taxonomic analysis preceding the analysis of the cognitive frames underlying those

Here we propose to adapt the RCA process in order to use only posets of concepts introducing objects or attributes (AOC-poset) rather than to build full concept lattices at each step