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01941nma a2200325 u 4500 |
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EB001992541 |
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EBX01000000000000001155443 |
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| 007 |
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| 008 |
210512 ||| eng |
| 020 |
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|a 9783039369584
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| 020 |
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|a 9783039369591
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| 020 |
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|a books978-3-03936-959-1
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| 100 |
1 |
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|a Pintelas, Panagiotis E.
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| 245 |
0 |
0 |
|a Ensemble Algorithms and Their Applications
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (182 p.)
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| 653 |
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|a Information technology industries / bicssc
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| 700 |
1 |
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|a Livieris, Ioannis E.
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| 700 |
1 |
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|a Pintelas, Panagiotis E.
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| 700 |
1 |
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|a Livieris, Ioannis E.
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
0 |
|a 10.3390/books978-3-03936-959-1
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/69084
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/2853
|7 0
|x Verlag
|3 Volltext
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| 082 |
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|a 000
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| 082 |
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|a 600
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| 520 |
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|a In recent decades, the development of ensemble learning methodologies has gained a significant attention from the scientific and industrial community, and found their application in various real-word problems. Theoretical and experimental evidence proved that ensemble models provide a considerably better prediction performance than single models. The main aim of this collection is to present the recent advances related to ensemble learning algorithms and investigate the impact of their application in a diversity of real-world problems. All papers possess significant elements of novelty and introduce interesting ensemble-based approaches, which provide readers with a glimpse of the state-of-the-art research in the domain.
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