Optimizing Hyper Meta Learning Models: An Epic

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Author(s): G. Devika (Government Engineering College, Krishnarajapete, India)and Asha Gowda Karegowda (Siddaganga Institute of Technology, India)
Copyright: 2023
Pages: 33
Source title: Meta-Learning Frameworks for Imaging Applications
Source Author(s)/Editor(s): Ashok Sharma (University of Jammu, India), Sandeep Singh Sengar (Cardiff Metropolitan University, UK)and Parveen Singh (Cluster University, Jammu, India)
DOI: 10.4018/978-1-6684-7659-8.ch003

Keywords: Cognitive Informatics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Optimizing hyper meta learning models is a critical task in the field of machine learning, as it can improve the performance, efficiency, and scalability of these models. In this chapter, the authors present an epic overview of the process of optimizing hyper meta learning models. They discuss the key steps involved in this process, including task selection, model architecture selection, hyperparameter optimization, model training, model evaluation, and deployment. They also explore the benefits of hyper meta learning models and their potential future applications in various fields. Finally, they highlight the challenges and limitations of hyper meta learning models and suggest future research directions to overcome these challenges and improve the effectiveness of these models.

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Optimizing Hyper Meta Learning Models: An Epic

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