gBoost: a mathematical programming approach to graph classification and regression

DOI10.1007/s10994-008-5089-zzbMath1470.68167OpenAlexW2148611932MaRDI QIDQ1959643

Koji Tsuda, Taku Kudo, Tadashi Kadowaki, Hiroto Saigo, Sebastian Nowozin

Publication date: 7 October 2010

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-008-5089-z

zbMATH Keywords

classificationmathematical programmingQSARregressiongraph mining

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Pattern recognition, speech recognition (68T10)

Related Items (10)

Graph-valued regression: prediction of unlabelled networks in a non-Euclidean graph space ⋮ Supervised classification and mathematical optimization ⋮ Finding the best not the most: regularized loss minimization subgraph selection for graph classification ⋮ Fast learning of relational kernels ⋮ Sublinear update time randomized algorithms for dynamic graph regression ⋮ Discriminative frequent subgraph mining with optimality guarantees ⋮ gBoost ⋮ Distance metric learning for graph structured data ⋮ On the theory of dynamic graph regression problem ⋮ Shuffled graph classification: theory and connectome applications

Uses Software

• AFGen
• AdaBoost.MH
• gSpan
• C4.5

Cites Work

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• Wrappers for feature subset selection
• Stabilized column generation
• A decision-theoretic generalization of on-line learning and an application to boosting
• Biological Sequence Analysis
• Learning Theory and Kernel Machines
• Regularization and Variable Selection Via the Elastic Net
• Linear programming boosting via column generation

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