Evolving large-scale data stream analytics based on scalable PANFIS
The main challenge in large-scale data stream analytics lies in the ability of machine learning to generate large-scale data knowledge in reasonable timeframe without suffering from a loss of accuracy. Many distributed machine learning frameworks have recently been built to speed up the large-scale...
Saved in:
Main Authors: | Za'in, Choiru, Pratama, Mahardhika, Pardede, Eric |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/151672 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Density-based clustering of data streams at multiple resolutions
by: Wan, Li
Published: (2013) -
CASTLE: Continuously anonymizing data streams
by: Cao, J., et al.
Published: (2013) -
Exploring time related issues in data stream processing
by: WU JI
Published: (2011) -
X-Fuzz: an evolving and interpretable neurofuzzy learner for data streams
by: Ferdaus, Md Meftahul, et al.
Published: (2024) -
Data-driven memory management for stream join
by: Wu, J., et al.
Published: (2013)