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  • "Classification is essential in statistical learning. This thesis deals with three topics in classification: multi-label classification, nonparametric multi-class classification and a special type of text categorization called occupation coding. For each topic, novel approaches are presented with the goal of high predictive performance. This is empirically demonstrated for each method. […] Occupation coding is an important multi-class text categorization problem. Since fully automated classification is challenging, researchers focus more on partially automated coding. Three approaches based on underlying statistical learning methods are proposed to improve the classification accuracy of the underlying statistical learning methods."Die ALLBUS-Daten 2006 werden für die Analyse verwendet. (xsd:string)
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  • (ALLBUS) (xsd:string)
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  • ALLBUS-Bibliography (xsd:string)
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  • Aufgenommen: 32. Fassung, März 2018 (xsd:gyear)
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  • 2017 (xsd:gyear)
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  • 2017 (xsd:gyear)
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  • english (xsd:string)
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?:name
  • Statistical Learning Approaches to Some Classification Problems (xsd:string)
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  • phdthesis (xsd:string)
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  • Bibsonomy (xsd:string)
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  • ALLBUS (xsd:string)
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  • 2017 (xsd:string)
  • ALLBUS (xsd:string)
  • ALLBUS2006 (xsd:string)
  • ALLBUS_input2017 (xsd:string)
  • ALLBUS_pro (xsd:string)
  • ALLBUS_version32 (xsd:string)
  • FDZ_ALLBUS (xsd:string)
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  • english (xsd:string)
  • phdthesis (xsd:string)
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?:uploadDate
  • 18.10.2017 (xsd:gyear)
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