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  • Inverse Functional Properties: Disabled (fastest) Apply to subjects only Apply to objects only Apply to both subjects and objects
  • "Same As": Disabled (fastest) Apply to subjects only Apply to objects only Apply to both subjects and objects (recommended) Apply to subjects, objects and predicates (not recommended on big datasets) Apply to predicates only (special use cases only)

About: Vowpal Wabbit     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : yago:WikicatDataMiningAndMachineLearningSoftware, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)

Type: Thingsoftwareschema:CreativeWorkworkwikidata:Q386724Softwareyago:Abstraction100002137yago:Code106355894yago:CodingSystem106353757yago:Cognition100023271yago:Communication100033020yago:Datum105816622yago:Information105816287yago:PsychologicalFeature100023100yago:Writing106359877yago:WrittenCommunication106349220yago:Software106566077yago:WikicatDataMiningAndMachineLearningSoftware New Facet based on Instances of this Class

Vowpal Wabbit (VW) is an open-source fast online interactive machine learning system library and program developed originally at Yahoo! Research, and currently at Microsoft Research. It was started and is led by John Langford. Vowpal Wabbit's interactive learning support is particularly notable including Contextual Bandits, Active Learning, and forms of guided Reinforcement Learning. Vowpal Wabbit provides an efficient scalable out-of-core implementation with support for a number of machine learning reductions, importance weighting, and a selection of different loss functions and optimization algorithms.