On the training sample size and classification performance: An experimental evaluation in seismic facies classification
Machine learning algorithms (MLAs) perform better when enough high-quality training data is provided. However, a lack of training data is frequent in seismic facies classification and many other supervised learning applications. Data labeling for seismic facies classification is time-consuming and r...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Published: |
Elsevier B.V.
2023
|
Online Access: | http://scholars.utp.edu.my/id/eprint/37516/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159765394&doi=10.1016%2fj.geoen.2023.211809&partnerID=40&md5=bb201dd3f5ec9479db4d5d1224cf93c6 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|