  Multi-output Gaussian processes (MOGP) are probability distributions over
vector-valued functions, and have been previously used for multi-output
regression and for multi-class classification. A less explored facet of the
multi-output Gaussian process is that it can be used as a generative model for
vector-valued random fields in the context of pattern recognition. As a
generative model, the multi-output GP is able to handle vector-valued functions
with continuous inputs, as opposed, for example, to hidden Markov models. It
also offers the ability to model multivariate random functions with high
dimensional inputs. In this report, we use a discriminative training criteria
known as Minimum Classification Error to fit the parameters of a multi-output
Gaussian process. We compare the performance of generative training and
discriminative training of MOGP in emotion recognition, activity recognition,
and face recognition. We also compare the proposed methodology against hidden
Markov models trained in a generative and in a discriminative way.
