Kneib, Thomas

[["dc.bibliographiccitation.firstpage","2109"],["dc.bibliographiccitation.journal","Journal of Machine Learning Reseach - Machine Learning Open Source Software"],["dc.bibliographiccitation.lastpage","2113"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Hothorn, Torsten"],["dc.contributor.author","Bühlmann, Peter"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Hofner, Benjamin"],["dc.date.accessioned","2017-09-07T11:52:15Z"],["dc.date.available","2017-09-07T11:52:15Z"],["dc.date.issued","2010"],["dc.description.abstract","We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions using component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data."],["dc.identifier.fs","580723"],["dc.identifier.gro","3148161"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7553"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5508"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","chake"],["dc.rights.access","openAccess"],["dc.subject","component-wise functional gradient descent; splines, decision trees"],["dc.subject.ddc","330"],["dc.title","Model-based Boosting 2.0"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]