In this paper we study the k-means clustering problem. It is well-known that the general version of this problem is NP-hard. Numerous approximation algorithms have been proposed fo...
Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (`churn') at their convenience and without notification. This mak...
Dejan Radosavljevik, Peter van der Putten, Kim Kyl...
Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applic...
This paper proposes a new measure for ensemble pruning via directed hill climbing, dubbed Uncertainty Weighted Accuracy (UWA), which takes into account the uncertainty of the decis...
Ioannis Partalas, Grigorios Tsoumakas, Ioannis P. ...
Spectral analysis approaches have been actively studied in machine learning and data mining areas, due to their generality, efficiency, and rich theoretical foundations. As a natur...
Dijun Luo, Heng Huang, Chris H. Q. Ding, Feiping N...
Traditional classification methods assume that the training and the test data arise from the same underlying distribution. However, in several adversarial settings, the test set is...
We introduce a general-purpose learning machine that we call the Guaranteed Error Machine, or GEM, and two learning algorithms, a real GEM algorithm and an ideal GEM algorithm. Th...
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applica...