Rejoinder on “Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization”

Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization

Methods for analyzing or learning from “fuzzy data” have attracted increasing attention in recent years. In many cases, however, existing methods (for precise, non-fuzzy data) are extended to the fuzzy case in an ad-hoc manner, and without carefully considering the interpretation of a fuzzy set when being used for modeling data. Distinguishing between an ontic and an epistemic interpretation of...

Learning from Imprecise and Fuzzy Data: on the Notion of Data Disambiguation

Superset Learning Based on Generalized Loss Minimization

In standard supervised learning, each training instance is associated with an outcome from a corresponding output space (e.g., a class label in classification or a real number in regression). In the superset learning problem, the outcome is only characterized in terms of a superset—a subset of candidates that covers the true outcome but may also contain additional ones. Thus, superset learning ...

the effect of vocabulary instruction through semantic mapping on learning and recall of efl learners

چکیده ندارد.

Learning from Imprecise Data: Possibilistic Graphical Models

Graphical models—especially probabilistic networks like Bayes networks and Markov networks—are very popular to make reasoning in highdimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets ...