- Hauptmenü-

Katalogdatenanzeige

Data Fusion in Information Retrieval

Kataloginformation Katalogdatensatz500170522 .

Kataloginformation
Feldname	Details
Vorliegende Sprache	eng
Hinweise auf parallele Ausgaben	365078298 Buchausg. u.d.T.: ‡Wu, Shengli: Data fusion in information retrieval
ISBN	978-3-642-28865-4
Name	Wu, Shengli
T I T E L	Data Fusion in Information Retrieval
Verlagsort	Berlin, Heidelberg
Verlag	Springer Berlin Heidelberg
Erscheinungsjahr	2012
Erscheinungsjahr	2012
Umfang	Online-Ressource (XII, 228p, digital)
Reihe	Adaptation, Learning, and Optimization ; 13
Notiz / Fußnoten	Description based upon print version of record
Weiterer Inhalt	Title; Preface; Contents; Introduction; The Rationale Behind Data Fusion; Several Typical Data Fusion Methods; Several Issues in Data Fusion; Evaluation of Retrieval Results; Binary Relevance Judgment; Incomplete Relevance Judgment; Graded Relevance Judgment; Score-Based Metrics; Uncertainty of Data Fusion Methods on Effectiveness Improvement; Score Normalization; Linear Score Normalization Methods; The Zero-One Linear Method; The Fitting Method; Normalizing Scores over a Group of Queries; Z-Scores; Sum-to-One; Comparison of Four Methods; Nonlinear Score Normalization Methods. The Normal-Exponential Mixture ModelThe CDF-Based Method; Bayesian Inference-Based Method; Transforming Ranking Information into Scores; Observing from the Data; The Borda Count Model; The Reciprocal Function of Rank; The Logistic Model; The Cubic Model; The Informetric Distribution; Empirical Investigation; Mixed Normalization Methods; Related Issues; Weights Assignment and Normalization; Other Relevance Judgments; Observations and Analyses; Prior Observations and Points of View; Performance Prediction of CombSum and CombMNZ; Data Fusion Performance; Improvement over Average Performance. Performance Improvement over Best PerformancePerformance Prediction; The Predictive Ability of Variables; Some Further Observations; Summary; Comparison of CombSum and CombMNZ; Applying Statistical Principles to Data Fusion; Further Discussion about CombSum and CombMNZ; Lee's Experiment Revisited; Performance Prediction of the Linear Combination Method; The Linear Combination Method; Performance Related Weighting; Empirical Investigation of Appropriate Weights; Other Features; Consideration of Uneven Similarity among Results; Correlation Methods 1 and 2; Correlation Methods 3 and 4. Correlation Methods 5 and 6Empirical Study; Some More Observations; Combined Weights; Theory of Stratified Sampling and Its Application; Experiments; Deciding Weights by Multiple Linear Regression; Empirical Investigation Setting; Experiment 1; Experiment 2; Experiment 3; Optimization Methods; Summary; A Geometric Framework for Data Fusion; Relevance Score-Based Data Fusion Methods and Ranking-Based Metrics; The Geometric Framework; The Centroid-Based Data Fusion Method; The Linear Combination Method; Relation between the Euclidean Distance and Ranking-Based Measures; Conclusive Remarks. Ranking-Based FusionBorda Count and Condorcet Voting; Weights Training for Weighted Condorcet Voting; Experimental Settings and Methodologies; Experimental Results of Fusion Performance; Positive Evidence in Support of the Hypothesis; Fusing Results from Overlapping Databases; Introduction to Federated Search; Resource Selection; A Basic Resource Selection Model; Solution to the Basic Resource Selection Model; Some Variants of the Basic Model and Related Solutions; Experiments and Experimental Results; Further Discussion; Results Merging; Several Result Merging Methods. Evaluation of Result Merging Methods
Titelhinweis	Buchausg. u.d.T.: ‡Wu, Shengli: Data fusion in information retrieval
ISBN	ISBN 978-3-642-28866-1
Klassifikation	UYQ
	COM004000
	*68-02
	68P20
	68U35
	68T37
	006.3
	005.74
	Q342
	ST 530
Kurzbeschreibung	Shengli Wu
2. Kurzbeschreibung	The 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?
1. Schlagwortkette	Information Retrieval
1. Schlagwortkette	Datenfusion
1. Schlagwortkette ANZEIGE DER KETTE	Information Retrieval -- Datenfusion
2. Schlagwortkette	Information Retrieval
2. Schlagwortkette	Datenfusion
ANZEIGE DER KETTE	Information Retrieval -- Datenfusion
SWB-Titel-Idn	365270180
Signatur	Springer E-Book
Bemerkungen	Elektronischer Volltext - Campuslizenz
Elektronische Adresse	$uhttp://dx.doi.org/10.1007/978-3-642-28866-1
Internetseite / Link	Volltext
Siehe auch	Volltext
Siehe auch	Inhaltstext

ISBD-Anzeige Katalogdatensatz500170522

Kategorien-Anzeige Katalogdatensatz500170522

Titel zur Titelsammlung hinzufügen Katalogdatensatz500170522

Kataloginformation500170522 Datensatzanfang . Kataloginformation500170522 Seitenanfang .

Titel vormerken Katalogdatensatz500170522

Vollanzeige Katalogdaten

Auf diesem Bildschirm erhalten Sie Katalog- und Exemplarinformationen zum ausgewählten Titel.

Im Bereich Kataloginformation werden die bibliographischen Details angezeigt. Per Klick auf Hyperlink-Begriffe wie Schlagwörter, Autoren, Reihen, Körperschaften und Klassifikationen können Sie sich weitere Titel des gewählten Begriffes anzeigen lassen.

Der Bereich Exemplarinformationen enthält zum einen Angaben über den Standort und die Verfügbarkeit der Exemplare. Zum anderen haben Sie die Möglichkeit, ausgeliehene Exemplare vorzumerken oder Exemplare aus dem Magazin zu bestellen.