We consider the problem of anomaly detection in multiple co-evolving data streams. In this paper, we introduce FRAHST (Fast Rank-Adaptive row-Householder Subspace Tracking). It au...
Pedro Henriques dos Santos Teixeira, Ruy Luiz Mili...
Abstract. Boosting methods are known to improve generalization performances of learning algorithms reducing both bias and variance or enlarging the margin of the resulting multi-cl...
Francesco Masulli, Matteo Pardo, Giorgio Sbervegli...
We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed computation, where ...
David Newman, Arthur Asuncion, Padhraic Smyth, Max...
— The application of Qualitative Reasoning to Learning Algorithms can provide these models with the capability of automate common-sense and expert reasoning. Learning algorithms ...
We examine data collected from on-line assessments of the numeracy and literacy skills of young students in order to construct probabilistic agent-based controllers. We demonstrat...
Elizabeth Sklar, Jordan Salvit, Christopher Camach...