Author ORCID Identifier
0000-0002-3912-1470
Document Type
Conference Paper
Disciplines
Statistics
Abstract
Inadequate data complicates planning and allocation of VoD resources, potentially hindering the scalability of VoD services. We propose a Transfer Learning Load Adjusted (TLLA) algorithm for resource management given limited VoD data. TLLA leverages the knowledge gained from pre-trained models by storing features and patterns that can be used to train Machine Learning (ML) related tasks. We model limitations in VoD data by proportionally freezing 50% of the neural layers in models trained from pre-trained and source domains. We evaluate the performance of the frozen neural layers by comparing them to unfrozen data. Freezing 50% of the neural layers in pre-trained model shows ≈ 80% similarity to model frozen from source data. These findings have the potential to contribute knowledge that could be utilized when inadequacies in VoD resources are encountered.
DOI
https://doi.org/10.21427/szb5-t470
Recommended Citation
Kimeli, Kangogo and de Fréin, Ruairí, "A Transfer Learning Load Adjusted Approach for Video-on-Demand Systems Given Limited Training Data" (2025). SAML-25 Workshop on Statistical and Machine Learning. 20.
https://arrow.tudublin.ie/saml/20
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025 at TU Dublin, Ireland.
doi:10.21427/szb5-t470