In this paper, we discuss a technique for handling multi-class problems with binary classifiers, namely to learn one classifier for each pair of classes. Although this idea is kno...
The paper presents an evaluation method for e-learning platforms, based on different types of measurements collected in logs of interactions during learning sessions, and on the an...
Vlad Posea, Dan Mihaila, Stefan Trausan-Matu, Vale...
We study the problem of private classification using kernel methods. More specifically, we propose private protocols implementing the Kernel Adatron and Kernel Perceptron learning ...
This paper describes a system combining model-based and learning-based methods for automated reasoning in large theories, i.e. on a large number of problems that use many axioms, l...
Data-driven learning based on shift reduce parsing algorithms has emerged dependency parsing and shown excellent performance to many Treebanks. In this paper, we investigate the e...