Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines....
In this paper we redefine and generalize the classic k-nearest neighbors (k-NN) voting rule in a Bayesian maximum-a-posteriori (MAP) framework. Therefore, annotated examples are u...
Paolo Piro, Richard Nock, Frank Nielsen, Michel Ba...
: In this paper we propose an application of data mining methods in the prediction of the availability and performance of Internet paths. We deploy a general decision-making method...
We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded b...
In this paper, we present a 3D X-Ray Transform based multilinear feature extraction and classification method for Digital Multi-focal Images (DMI). In such images, morphological i...