Fast Forward Sele tion to Speed Up Sparse Gaussian Pro essRegressionMatthias

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

We present a method for the sparse greedy approximation of Bayesian Gaussian process regression, featuring a novel heuristic for very fast forward selection. Our method is essentially as fast as an equivalent one which selects the “support” patterns at random, yet it can outperform random selection on hard curve fitting tasks. More importantly, it leads to a sufficiently stable approximation of...

Fast Reconstruction of SAR Images with Phase Error Using Sparse Representation

In the past years, a number of algorithms have been introduced for synthesis aperture radar (SAR) imaging. However, they all suffer from the same problem: The data size to process is considerably large. In recent years, compressive sensing and sparse representation of the signal in SAR has gained a significant research interest. This method offers the advantage of reducing the sampling rate, bu...

Autonomous agent learning using an actor-critic algorithm and behavior models

We introdu e a Supervised Reinfor ement Learning (SRL) algorithm for autonomous learning problems where an agent is required to deal with high dimensional spa es. In our learning algorithm, behavior models learned from a set of examples, are used to dynami ally redu e the set of relevant a tions at ea h state of the environment en ountered by the agent. Su h subsets of a tions are used to guide...

Speeding up the binary Gaussian process classification

Gaussian processes (GP) are attractive building blocks for many probabilistic models. Their drawbacks, however, are the rapidly increasing inference time and memory requirement alongside increasing data. The problem can be alleviated with compactly supported (CS) covariance functions, which produce sparse covariance matrices that are fast in computations and cheap to store. CS functions have pr...

1 The Use of Subset Sele tion in Combined -