Posts Tagged ‘Classification’

Ali Rahimi and Ben Recht:
Random Features for Large Scale Kernel Machines
NIPS 2007

In this paper, the authors propose to map data to a low-dimensional Euclidean space, such that the inner product in this space is a close approximation of the inner product computed by a stationary (shift-invariant) kernel (in a potentially infinite-dimensional RKHS). The approach is based on Bochner’s theorem.

The central equation is this one: (more…)


Daniel Hernandez-Lobato and Jose Miguel Hernandez-Lobato:
Scalable Gaussian Process Classification via Expectation Propagation

The paper is about large-scale Gaussian process classification. Unlike many others, the authors use Expectation Propagation (and not Variational Inference) for approximate inference. An approximate marginal likelihood expression is derived that factorizes over the data instances, which allows for distributed inference and training. Training is additionally sped up by using mini-batches of data.

An interesting part of the paper is that (more…)