Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel ...
A multitask learning framework is developed for discriminative classification and regression where multiple large-margin linear classifiers are estimated for different predictio...
We present a novel language identification technique using our recently developed deep-structured conditional random fields (CRFs). The deep-structured CRF is a multi-layer CRF mo...
Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision p...
We propose a novel approach to modeling prosodic features. Inspired by Joint Factor Analysis model (JFA), our model is based on the same idea of introducing subspace of model para...
Marcel Kockmann, Lukas Burget, Ondrej Glembek, Luc...