Nature-Inspired Computation in Data Mining and Machine Learning
| Collectivité auteur: | |
|---|---|
| Autres auteurs: | , |
| Résumé: | XI, 273 p. 87 illus., 66 illus. in color. text |
| Langue: | anglais |
| Publié: |
Cham :
Springer International Publishing : Imprint: Springer,
2020.
|
| Édition: | 1st ed. 2020. |
| Collection: | Studies in Computational Intelligence,
855 |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/978-3-030-28553-1 |
| Format: | Électronique eBook |
Table des matières:
- Adaptive Improved Flower Pollination Algorithm for Global Optimization
- Algorithms for Optimization and Machine Learning over Cloud
- Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks
- Comparative analysis of different classifiers on crisis-related tweets: An elaborate study
- An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm
- Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services.