Search the World's Largest Database of Information Science & Technology Terms & Definitions
InfInfoScipedia LogoScipedia
A Free Service of IGI Global Publishing House
Below please find a list of definitions for the term that
you selected from multiple scholarly research resources.

What is Hyperparameter

Chapter 11
Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning
These are the parameters that define the model itself and have to be set before a model can be trained. These are different from the parameters that the model learns during training such as node weights.
Published in Chapter:
Model Optimisation Techniques for Convolutional Neural Networks
Sajid Nazir (Glasgow Caledonian University, UK), Shushma Patel (De Montfort University, UK), and Dilip Patel (London South Bank University, UK)
Copyright: © 2022 |Pages: 30
DOI: 10.4018/978-1-7998-8686-0.ch011
Abstract
Deep neural networks provide good results for computer vision tasks. This has been possible due to a renewed interest in neural networks, availability of large-scale labelled training data, virtually unlimited processing and storage on cloud platforms and high-performance clusters. A convolutional neural network (CNN) is one such architecture better suited for image classification. An important factor for a better CNN performance, besides the data quality, is the choice of hyperparameters, which define the model itself. The model or hyperparameter optimisation involves selecting the best configuration of hyperparameters but is challenging because the set of hyperparameters are different for each type of machine learning algorithm. Thus, it requires a lot of computational time and resources to determine a better performing machine learning model. Therefore, the process has a lot of research interest, and currently a transition to a fully automated process is also underway. This chapter provides a survey of the CNN model optimisation techniques proposed in the literature.
Full Text Chapter Download: US $37.50 Add to Cart
More Results
Chapter 175
Latent Dirichlet Allocation Approach for Analyzing Text Documents
A hyperparameter is a parameter of a prior distribution of a variable where the prior distribution is the probability distribution that expresses the uncertainty of the variable before the data is taken into account.
Published in Chapter: Latent Dirichlet Allocation Approach for Analyzing Text Documents; From: Encyclopedia of Information Science and Technology, Third Edition
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 60
Conditional Hazard Estimating Neural Networks
Parameter in a hierarchical problem formulation. In Bayesian inference, the parameters of a prior.
Published in Chapter: Conditional Hazard Estimating Neural Networks; From: Encyclopedia of Artificial Intelligence
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 4
Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection
All the variables that a user can set before starting the training, which are tunable and can directly affect how well an algorithm learns.
Published in Chapter: Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection; From: Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 11
Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings
These are model parameters that can be manually configured (through expert judgment or empirical results) to achieve optimal performance. Every machine learning model has its own set of hyperparameters.
Published in Chapter: Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
Full Text Chapter Download: US $37.50 Add to Cart
eContent Pro Discount Banner
InfoSci OnDemandECP Editorial ServicesAGOSR