This paper presents the development of two related machine-learned models which predict (a) whether a student can answer correctly questions in an ILE without requesting help and (...
The class imbalance problem (when one of the classes has much less samples than the others) is of great importance in machine learning, because it corresponds to many critical app...
We present an approach for learning part-of-speech distinctions by induction over the lexicon of the Cyc knowledge base. This produces good results (74.6%) using a decision tree t...
Homograph ambiguity is an original issue in Text-to-Speech (TTS). To disambiguate homograph, several efficient approaches have been proposed such as part-of-speech (POS) n-gram, B...
: Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper we employ combination of Adaboost with Support Vector Machine (SVM) as comp...