Few existing argumentation frameworks are designed to deal with probabilistic knowledge, and none are designed to represent possibilistic knowledge, making them unsuitable for man...
We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to estab...
This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We argue that constraining the mapping between the high a...
The recent years have witnessed a surge of interests of semi-supervised clustering methods, which aim to cluster the data set under the guidance of some supervisory information. U...
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended...