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Magic-angle spinning pulsed field gradient nuclear magnetic resonance (MAS PFG NMR) was applied for selective self-diffusion measurements of acetone-n-alkane (C(6) up to C(9)) mixtures in nanoporous silica gel. Two specimens of silica gel with mean pore sizes of about 4 and 10 nm are considered. In the smaller pores, the n-alkane diffusivities are by about(More)
Within probabilistic classification problems, learning the Markov boundary of the class variable consists in the optimal approach for feature subset selection. In this paper we propose two algorithms that learn the Markov boundary of a selected variable. These algorithms are based on the score+search paradigm for learning Bayesian networks. Both algorithms(More)
We consider the scenario in which an automatic classifier (previously built) is available. It is used to classify new instances but, in some cases, the classifier may request the intervention of a human (the oracle), who gives it the correct class. In this scenario, first it is necessary to study how the performance of the system should be evaluated, as it(More)
1. Introduction Combination of pulsed field gradient (PFG) NMR with magic-angle spinning (MAS) NMR is demonstrated to have remarkable advantages in comparison with conventional PFG NMR, if applied to diffusion measurements in beds of nanoporous particles, notably zeolites. These advantages are based on the effect of line narrowing and concern (i) a(More)
Feature subset selection is increasingly becoming an important preprocessing step within the field of automatic classification. This is due to the fact that the domain problems currently considered contain a high number of variables, and some kind of dimensionality reduction becomes necessary, in order to make the classification task approachable. In this(More)
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