As the rise of pen-enabled interfaces is accompanied with an increased number of techniques for recognition of penbased input, recent trends in symbol recognition show an escalation in systems complexity (number of features, classifiers combination) or the over-specialization of systems to specific datasets or applications. In spite of the importance of representation space in feature-based met...

Approaching temporal link labelling as a classification task has already been explored in several works. However, choosing the right feature vectors to build the classification model is still an open issue, especially for event-event classification, whose accuracy is still under 50%. We find that using a simple feature set results in a better performance than using more sophisticated features b...

Conventionally, tracking people through an environment has been achieved by monitoring a series of fixed cameras. With the advent of wireless technologies, the option of inverting the paradigm and monitoring instead, from each person’s pointof-view, has become more accessible. By taking video sequences from a person moving through various environments, this paper explores a process for classify...

In this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentenceto-λ-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the λlogical expression structure into a form suitable for the mechanics of sta...

In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models, have ordinary performance but strong interpretability on a set of simple features. The other series, including tree-based models, organize numerical, categorical and high dimensional features into a comprehensive str...