Nature-Inspired Computation in Data Mining and Machine Learning
| Ente Autore: | |
|---|---|
| Altri autori: | , |
| Riassunto: | XI, 273 p. 87 illus., 66 illus. in color. text |
| Lingua: | inglese |
| Pubblicazione: |
Cham :
Springer International Publishing : Imprint: Springer,
2020.
|
| Edizione: | 1st ed. 2020. |
| Serie: | Studies in Computational Intelligence,
855 |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-3-030-28553-1 |
| Natura: | Elettronico Libro |
Sommario:
- 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.