In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and stud...
Abstract. We describe the use of non-parametric permutation tests to detect activation in cortically-constrained maps of current density computed from MEG data. The methods are app...
Dimitrios Pantazis, Thomas E. Nichols, Sylvain Bai...
Background: The ability to design thermostable proteins is theoretically important and practically useful. Robust and accurate algorithms, however, remain elusive. One critical pr...
In the paper, we suggest a set of visualization-based exploratory tools to support analysis and comparison of different spatial development scenarios, such as results of simulatio...
Natalia V. Andrienko, Gennady L. Andrienko, Peter ...
There are a variety of methods for inducing predictive systems from observed data. Many of these methods fall into the field of study of machine learning. Some of the most effec...