A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
In the literature, it is commonly believed that learning from few data problem can be resolved by using classifiers that consider interclass relationships. In this work, we will adopt this point of view in learning from few sparse textual data, essentially, by considering the sparseness of the latter as a good support for inducing theories about generalization. Therefore, we opt for an inductive approach based on combining: evidence-based analysis of patterns, logic and preferences. Moredoi:10.14236/ewic/isiict2009.2 fatcat:m3xwll72wvampmsm276ku7j4tq