000 02701nam a22004695i 4500
001 978-3-642-28866-1
003 DE-He213
005 20140220083314.0
007 cr nn 008mamaa
008 120405s2012 gw | s |||| 0|eng d
020 _a9783642288661
_9978-3-642-28866-1
024 7 _a10.1007/978-3-642-28866-1
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aWu, Shengli.
_eauthor.
245 1 0 _aData Fusion in Information Retrieval
_h[electronic resource] /
_cby Shengli Wu.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2012.
300 _aXII, 228p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdaptation, Learning, and Optimization,
_x1867-4534 ;
_v13
505 0 _aIntroduction -- Evaluation of Retrieval Results -- Score Normalization -- Observations and Analyses -- The Linear Combination Method -- A Geometric Framework for Data Fusion -- Ranking-Based Fusion -- Fusing Results from Overlapping Databases -- Application of the Data Fusion Technique.
520 _aThe technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others: -          What are the key factors that affect the performance of data fusion algorithms significantly? -          What conditions are favorable to data fusion algorithms? -          CombSum and CombMNZ, which one is better? and why? -          What is the rationale of using the linear combination method? -          How can the best fusion option be found under any given circumstances?
650 0 _aEngineering.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aData Mining and Knowledge Discovery.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642288654
830 0 _aAdaptation, Learning, and Optimization,
_x1867-4534 ;
_v13
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-28866-1
912 _aZDB-2-ENG
999 _c102907
_d102907