Learning To Learn: A Survey of Recent Literature on Meta Learning
Abstract
The essence of Meta-learning is “learning to learn”. Meta Learning is a subset of machine learning. Meta-learning is the process of using knowledge gained from many tasks during meta-training to enable a model to quickly learn new tasks from few examples. Meta learning algorithm, or the learning method itself, such that the modified learner is better than the original learner at learning from additional experience. This paper explore introduction of Meta learning, how it works , the structure of literature survey of Meta learning, Meta Learning for few shot learning in specialized domains, evaluation metrics and benchmark datasets for meta learning and future direction and open problems in meta learning for few shot learning.
Fields of Study & Subjects
Domain Adaptation and Few-Shot Learning
Machine Learning and Algorithms
Topic Modeling
Publication details
- Journal / source
- International Journal on Advanced Computer Theory and Engineering
- Volume
- 15
- Issue
- 1S
- Pages
- 42–57
- Publication date
- 18 January 2026
- Language
- EN