With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive ...
While most supervised machine learning models assume that training examples are sampled at random or adversarially, this article is concerned with models of learning from a cooper...
Sandra Zilles, Steffen Lange, Robert Holte, Martin...
Background: Accurate identification of protein domain boundaries is useful for protein structure determination and prediction. However, predicting protein domain boundaries from a...
We consider the general, widely applicable problem of selecting from n real-valued random variables a subset of size m of those with the highest means, based on as few samples as ...
—This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature selection algorithm that addres...