Bayesian classification with Gaussian processes

We consider the problem of assigning an input vector to one of m classes by predicting P(c|x) for c=1,...,m. For a two-class problem, the probability of class one given x is estimated by s(y(x)), where s(y)=1/(1+e-y). A Gaussian process prior is placed on y(x), and is combined with the training data to obtain predictions for new x points. We provide a Bayesian treatment, integrating over uncertainty in y and in the parameters that control the Gaussian process prior the necessary integration over y is carried out using Laplace's approximation. The method is generalized to multiclass problems (m>2) using the softmax function. We demonstrate the effectiveness of the method on a number of datasets.

Experimental implementation of an all-optical interferometric drop, add, and extract multiplexer for superchannels

We present the experimental implementation of an all-optical ROADM scheme for routing of an individual subchannel within an all-optical OFDM superchannel. The different functions required of optical node were demonstrated using interferometric technique with the extraction, drop, and addition of individual subchannel in a ten subchannels optically aggregated signal. The scheme we reported enables a fully flexible node compatible with future terabit per second superchannel transmission.