A recursive least squares training approach for convolutional neural networks
| dc.contributor.author | Yang, Yifan, author | |
| dc.contributor.author | Azimi-Sadjadi, Mahmood, advisor | |
| dc.contributor.author | Pezeshki, Ali, committee member | |
| dc.contributor.author | Oprea, Iuliana, committee member | |
| dc.date.accessioned | 2022-05-30T10:21:10Z | |
| dc.date.available | 2022-05-30T10:21:10Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | This thesis aims to come up with a fast method to train convolutional neural networks (CNNs) using the application of the recursive least squares (RLS) algorithm in conjunction with the back-propagation learning. In the training phase, the mean squared error (MSE) between the actual and desired outputs is iteratively minimized. The recursive updating equations for CNNs are derived via the back-propagation method and normal equations. This method does not need the choice of a learning rate and hence does not suffer from speed-accuracy trade-off. Additionally, it is much faster than the conventional gradient-based methods in a sense that it needs less epochs to converge. The learning curves of the proposed method together with those of the standard gradient-based methods using the same CNN structure are generated and compared on the MNIST handwritten digits and Fashion-MNIST clothes databases. The simulation results show that the proposed RLS-based training method requires only one epoch to meet the error goal during the training phase while offering comparable accuracy on the testing data sets. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Yang_colostate_0053N_17036.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235171 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | recursive least squares | |
| dc.subject | convolutional neural networks | |
| dc.title | A recursive least squares training approach for convolutional neural networks | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Yang_colostate_0053N_17036.pdf
- Size:
- 1.47 MB
- Format:
- Adobe Portable Document Format