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ML
2008
ACM
146views Machine Learning» more  ML 2008»
8 years 12 months ago
Improving maximum margin matrix factorization
Abstract. Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful lear...
Markus Weimer, Alexandros Karatzoglou, Alex J. Smo...
GCB
2009
Springer
139views Biometrics» more  GCB 2009»
9 years 3 months ago
Graph-Kernels for the Comparative Analysis of Protein Active Sites
Abstract: Graphs are often used to describe and analyze the geometry and physicochemical composition of biomolecular structures, such as chemical compounds and protein active sites...
Thomas Fober, Marco Mernberger, Ralph Moritz, Eyke...
CIDM
2009
IEEE
9 years 3 months ago
Empirical comparison of graph classification algorithms
The graph classification problem is learning to classify separate, individual graphs in a graph database into two or more categories. A number of algorithms have been introduced fo...
Nikhil S. Ketkar, Lawrence B. Holder, Diane J. Coo...
ICDM
2005
IEEE
142views Data Mining» more  ICDM 2005»
9 years 5 months ago
Shortest-Path Kernels on Graphs
Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available b...
Karsten M. Borgwardt, Hans-Peter Kriegel
ICPR
2008
IEEE
9 years 6 months ago
Alternative similarity functions for graph kernels
Given a bipartite graph of collaborative ratings, the task of recommendation and rating prediction can be modeled with graph kernels. We interpret these graph kernels as the inver...
Jérôme Kunegis, Andreas Lommatzsch, C...
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