Ensemble Algorithms and Their Applications
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 b...
Saved in:
| Other Authors: | , |
|---|---|
| Format: | Book Chapter |
| Published: |
Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2020
|
| Subjects: | |
| Online Access: | Get Fullteks DOAB: description of the publication |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| Physical Description: | 1 electronic resource (182 p.) |
| ISBN: | books978-3-03936-959-1 9783039369584 9783039369591 |
| Access: | Open Access |