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SDM
2009
SIAM
149views Data Mining» more  SDM 2009»
14 years 6 months ago
Near-optimal Supervised Feature Selection among Frequent Subgraphs.
Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and an...
Alexander J. Smola, Arthur Gretton, Hans-Peter Kri...
SDM
2009
SIAM
175views Data Mining» more  SDM 2009»
14 years 6 months ago
Low-Entropy Set Selection.
Most pattern discovery algorithms easily generate very large numbers of patterns, making the results impossible to understand and hard to use. Recently, the problem of instead sel...
Hannes Heikinheimo, Jilles Vreeken, Arno Siebes, H...
SDM
2009
SIAM
192views Data Mining» more  SDM 2009»
14 years 6 months ago
Mining Cohesive Patterns from Graphs with Feature Vectors.
The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to g...
Arash Rafiey, Flavia Moser, Martin Ester, Recep Co...
SDM
2009
SIAM
170views Data Mining» more  SDM 2009»
14 years 6 months ago
Optimal Distance Bounds on Time-Series Data.
Most data mining operations include an integral search component at their core. For example, the performance of similarity search or classification based on Nearest Neighbors is ...
Michail Vlachos, Philip S. Yu, Suleyman S. Kozat
SDM
2009
SIAM
119views Data Mining» more  SDM 2009»
14 years 6 months ago
Twin Vector Machines for Online Learning on a Budget.
This paper proposes Twin Vector Machine (TVM), a constant space and sublinear time Support Vector Machine (SVM) algorithm for online learning. TVM achieves its favorable scaling b...
Zhuang Wang, Slobodan Vucetic
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