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  • "... first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches - these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development." (Verlagsinformation) (xsd:string)
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  • 2011 (xsd:gyear)
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  • 2011 (xsd:gyear)
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  • Englisch (EN) (xsd:string)
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  • 9783642142666 ()
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  • Learning to rank for information retrieval (xsd:string)
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  • Buch (de)
  • Monographie (xsd:string)
  • book (en)
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  • Berlin: Springer, 2011.- XVII, 285 S., Ill., graph. Darst. (xsd:string)
  • GESIS-BIB (xsd:string)
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