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  • Rule: ActivityStreamsMapasEquivalentb3sb3sifpdbprdf-labelfacetshttp://dbpedia.org/resource/inference/rules/dbpedia#http://dbpedia.org/resource/inference/rules/opencyc#http://dbpedia.org/resource/inference/rules/umbel#http://dbpedia.org/resource/inference/rules/yago#http://dbpedia.org/schema/property_rules#http://www.ontologyportal.org/inference/rules/SUMO#http://www.ontologyportal.org/inference/rules/WordNet#http://www.w3.org/2002/07/owl#ldpoplwebskos-transurn:det:rdf:labelvirtrdf-labelvirtrdf-urlNone
    
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About: Context mixing     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : yago:Rule105846932, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)

Type: softwareyago:WikicatLosslessCompressionAlgorithmsyago:Abstraction100002137yago:Act100030358yago:Activity100407535yago:Algorithm105847438yago:Event100029378yago:Procedure101023820yago:PsychologicalFeature100023100yago:YagoPermanentlyLocatedEntityyago:Rule105846932 New Facet based on Instances of this Class

Context mixing is a type of data compression algorithm in which the next-symbol predictions of two or more statistical models are combined to yield a prediction that is often more accurate than any of the individual predictions. For example, one simple method (not necessarily the best) is to average the probabilities assigned by each model. The random forest is another method: it outputs the prediction that is the mode of the predictions output by individual models. Combining models is an active area of research in machine learning.