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MLDM
2010
Springer
9 years 1 months ago
Fast Algorithms for Constant Approximation k-Means Clustering
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...
Mingjun Song, Sanguthevar Rajasekaran
MLDM
2010
Springer
9 years 1 months ago
The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications: What to Predict, for Whom and Does t
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...
ML
2010
ACM
185views Machine Learning» more  ML 2010»
9 years 1 months ago
Learning to rank on graphs
Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applic...
Shivani Agarwal
ML
2010
ACM
147views Machine Learning» more  ML 2010»
9 years 1 months ago
An ensemble uncertainty aware measure for directed hill climbing ensemble pruning
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. ...
ML
2010
ACM
193views Machine Learning» more  ML 2010»
9 years 1 months ago
On the eigenvectors of p-Laplacian
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...
ML
2010
ACM
138views Machine Learning» more  ML 2010»
9 years 1 months ago
Mining adversarial patterns via regularized loss minimization
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...
Wei Liu, Sanjay Chawla
ML
2010
ACM
163views Machine Learning» more  ML 2010»
9 years 1 months ago
Classification with guaranteed probability of error
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...
Marco C. Campi
ML
2010
ACM
135views Machine Learning» more  ML 2010»
9 years 1 months ago
Multi-domain learning by confidence-weighted parameter combination
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...
Mark Dredze, Alex Kulesza, Koby Crammer
ML
2010
ACM
175views Machine Learning» more  ML 2010»
9 years 1 months ago
Concept learning in description logics using refinement operators
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...
Jens Lehmann, Pascal Hitzler
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