One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, ...
Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolop...
Supervised learning techniques for text classi cation often require a large number of labeled examples to learn accurately. One way to reduce the amountoflabeled datarequired is t...
Many semi-supervised learning algorithms only
deal with binary classification. Their extension to the
multi-class problem is usually obtained by repeatedly
solving a set of bina...
Scarcity and infeasibility of human supervision for large
scale multi-class classification problems necessitates active
learning. Unfortunately, existing active learning methods
...
Prateek Jain (University of Texas at Austin), Ashi...
We present a novel boosting algorithm where temporal consistency is addressed in a short-term way. Although temporal correlation of observed data may be an important cue for classi...
Pedro Canotilho Ribeiro, Plinio Moreno, José...