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?
| Autorius: | Shengli Wu |
| Serija: | Adaptation, Learning, and Optimization |
| Leidėjas: | Springer Berlin Heidelberg |
| Išleidimo metai: | 2014 |
| Knygos puslapių skaičius: | 240 |
| ISBN-10: | 3642448011 |
| ISBN-13: | 9783642448010 |
| Formatas: | 235 x 155 x 14 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
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