We study a generalized framework for structured sparsity. It extends the well known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as pa...
Luca Baldassarre, Jean Morales, Andreas Argyriou, ...
Many data sets are incomplete. For correct analysis of such data, one can either use algorithms that are designed to handle missing data or use imputation. Imputation has the bene...
Abstract. This paper presents a probabilistic model for combining cluster ensembles utilizing information theoretic measures. Starting from a co-association matrix which summarizes...
We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multipl...
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bow...
Recently, inductive databases (IDBs) have been proposed to tackle the problem of knowledge discovery from huge databases. With an IDB, the user/analyst performs a set of very diffe...