Aim of this paper is to address the problem of learning Boolean functions from training data with missing values. We present an extension of the BRAIN algorithm, called U-BRAIN (U...
In this paper we introduce a paradigm for learning in the limit of potentially infinite languages from all positive data and negative counterexamples provided in response to the ...
We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better...
Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. L...
—The problem we address in the paper is how to learn a joint representation from data lying on multiple manifolds. We are given multiple data sets and there is an underlying comm...