Lifelong Learning for Dynamic Churn Prediction

FIONI SARNEN

Informasi Dasar

20.04.4227
006.31
Karya Ilmiah - Skripsi (S1) - Reference

Learning continually, accumulating knowledge, and using it to learn new tasks was the characteristic of lifelong learning. Lifelong learning, which is also known as Continual Learning, takes benefit from the one that called the previous (first) task to solve the new task—this schema work by configuring a proper regularization between them. Elastic weight consolidation (EWC) method proposed by Google Deepmind provides a way of calculating the importance (preserving the previously acquired knowledge) of weight and selectively adjusts the plasticity. In this paper, the EWC is exploited to tackle the sequence tasks of predicting churn activity. The tasks involve two distinct datasets from the domain of Telecom. The experimental results show that EWC can elevate the model performance in sequence training. Lifelong learning offers a more flexible way of learning to further research in dynamic learning.

Subjek

Machine Learning
 

Katalog

Lifelong Learning for Dynamic Churn Prediction
 
-
Indonesia

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

FIONI SARNEN
Perorangan
Suyanto, Rita Rismala
 

Penerbit

Universitas Telkom, S1 Informatika
Bandung
2020

Koleksi

Kompetensi

 

Download / Flippingbook

 

Ulasan

Belum ada ulasan yang diberikan
anda harus sign-in untuk memberikan ulasan ke katalog ini