gBoost: a mathematical programming approach to graph classification and regression
type: Item
instance of: scholarly article
title: gBoost: a mathematical programming approach to graph classification and regression
zbMATH Open document ID: 1470.68167
author: Hiroto Saigo
author: Sebastian Nowozin
author: Tadashi Kadowaki
author: Taku Kudo
author: Koji Tsuda
published in: Machine Learning
publication date: 2010-10-07T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68R10
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 90C90
zbMATH DE Number: 5796898
zbMATH Keywords: graph mining
zbMATH Keywords: mathematical programming
zbMATH Keywords: classification
zbMATH Keywords: regression
zbMATH Keywords: QSAR
describes a project that uses: AFGen
describes a project that uses: AdaBoost.MH
describes a project that uses: gSpan
describes a project that uses: C4.5
MaRDI profile type: Publication
full work available at URL: s10994-008-5089-z
OpenAlex ID: W2148611932
cites work: Q4821526
cites work: Linear programming boosting via column generation
cites work: Stabilized column generation
cites work: Biological Sequence Analysis
cites work: A decision-theoretic generalization of on-line learning and an application to boosting
cites work: Learning Theory and Kernel Machines
cites work: Wrappers for feature subset selection
cites work: Q5566063
cites work: Q3044543
cites work: Q4864293
cites work: Regularization and Variable Selection Via the Elastic Net
DOI: 10.1007/S10994-008-5089-Z
description: scientific article; zbMATH DE number 5796898