Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common vari...
We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to loca...
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples ...
This paper proposes a new measure for ensemble pruning via directed hill climbing, dubbed Uncertainty Weighted Accuracy (UWA), which takes into account the uncertainty of the decis...
Ioannis Partalas, Grigorios Tsoumakas, Ioannis P. ...
This paper presents a novel paradigm for learning languages that consists of mapping strings to an appropriate high-dimensional feature space and learning a separating hyperplane i...