Log in to save to my catalogue

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparamete...

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparamete...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2729734825

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

About this item

Full title

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Publisher

West Yorkshire: Science and Information (SAI) Organization Limited

Journal title

International journal of advanced computer science & applications, 2022, Vol.13 (9)

Language

English

Formats

Publication information

Publisher

West Yorkshire: Science and Information (SAI) Organization Limited

More information

Scope and Contents

Contents

The multiple regression model is very popular among researchers in both field of social and science because it is easy to interpret and have a well-established theoretical framework. However, the multioutput multiple regression model is actually widely applied in the engineering field because in the industrial world there are many systems with mult...

Alternative Titles

Full title

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2729734825

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2729734825

Other Identifiers

ISSN

2158-107X

E-ISSN

2156-5570

DOI

10.14569/IJACSA.2022.0130992

How to access this item