UTILIZATION OF AUTO ENCODERS AS A GENERALIZATION METHOD FOR ANTI-SPOOFING CLASSIFICATION
Main Article Content
Abstract
When training large neural networks, explicit generalisation procedures are required to
ensure that the model's predictions can be generalised. Auto-encoders now have a new
generalisation result. As a result of the data's limited diversity, generalisation issues arise
when developing models aimed at tackling these types of problems. Regularization is often
added by including a regularisation parameter in the objective function; however, in this
paper, we will provide an alternative regularisation approach that is equal to the one
commonly used. The fact that other regularisation strategies failed to eliminate overfitting
implies that these regularisation techniques may be used as a pre-processing step for other
algorithms.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
