A hybrid Deep Boltzmann Functional Link Network for classification problems
© 2016 IEEE.This paper proposes a hybrid deep learning algorithm, namely, the Deep Boltzmann Functional Link Network (DBFLN) for classification problems. A Deep Boltzmann Machine (DBM) with two layers of Restricted Boltzmann Machine is the generative model that is used to generate stochastic feature...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/52864 |
| _version_ | 1848759030237888512 |
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| author | Savitha, R. Chan, Kit Yan San, P. Ling, S. Suresh, S. |
| author_facet | Savitha, R. Chan, Kit Yan San, P. Ling, S. Suresh, S. |
| author_sort | Savitha, R. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2016 IEEE.This paper proposes a hybrid deep learning algorithm, namely, the Deep Boltzmann Functional Link Network (DBFLN) for classification problems. A Deep Boltzmann Machine (DBM) with two layers of Restricted Boltzmann Machine is the generative model that is used to generate stochastic features and input weights for the discriminative model. A discriminative Functional Link Network (FLN) uses these features to approximate the nonlinear relationship between a set of features and their classes. FLN has three layers, namely, the input layer, the enhancement layer and the output layer. In a DBFLN, the features generated at the two hidden layers of the DBM act as the input features and the enhancement layer responses of the FLN. The output weights of the FLN are then estimated as a solution to a linear programming problem through pseudo-inverse. We first evaluate the performance of the DBFLN on three benchmark multi-category classification problems from the UCI machine learning repository, namely, the image segmentation problem, the vehicle classification problem and the glass identification problem. Performance study results on the benchmark classification problems show that DBFLN is an efficient classifier. We then use the DBFLN to classify the images in the TID2013 data set, based on their depth of distortions. The TID2013 data set comprises of 25 images, each with 5 levels of 24 distortion types. In all, the data set has 3000 images, which can be classified based on the depth of distortion. Thus, the IQA classification problem is defined as classifying the distorted images into one of the 5 classes (depending on the depth of distortion) using human visual image metrics as the input features. The performance of the DBFLN in classifying the image quality is compared with those of Support Vector Machines, Extreme Learning Machines, Random Vector Functional Link Network, and Deep Belief Network. Performance studies show the superior classification ability of the DBFLN. |
| first_indexed | 2025-11-14T09:53:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-52864 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:53:24Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-528642017-09-13T15:38:23Z A hybrid Deep Boltzmann Functional Link Network for classification problems Savitha, R. Chan, Kit Yan San, P. Ling, S. Suresh, S. © 2016 IEEE.This paper proposes a hybrid deep learning algorithm, namely, the Deep Boltzmann Functional Link Network (DBFLN) for classification problems. A Deep Boltzmann Machine (DBM) with two layers of Restricted Boltzmann Machine is the generative model that is used to generate stochastic features and input weights for the discriminative model. A discriminative Functional Link Network (FLN) uses these features to approximate the nonlinear relationship between a set of features and their classes. FLN has three layers, namely, the input layer, the enhancement layer and the output layer. In a DBFLN, the features generated at the two hidden layers of the DBM act as the input features and the enhancement layer responses of the FLN. The output weights of the FLN are then estimated as a solution to a linear programming problem through pseudo-inverse. We first evaluate the performance of the DBFLN on three benchmark multi-category classification problems from the UCI machine learning repository, namely, the image segmentation problem, the vehicle classification problem and the glass identification problem. Performance study results on the benchmark classification problems show that DBFLN is an efficient classifier. We then use the DBFLN to classify the images in the TID2013 data set, based on their depth of distortions. The TID2013 data set comprises of 25 images, each with 5 levels of 24 distortion types. In all, the data set has 3000 images, which can be classified based on the depth of distortion. Thus, the IQA classification problem is defined as classifying the distorted images into one of the 5 classes (depending on the depth of distortion) using human visual image metrics as the input features. The performance of the DBFLN in classifying the image quality is compared with those of Support Vector Machines, Extreme Learning Machines, Random Vector Functional Link Network, and Deep Belief Network. Performance studies show the superior classification ability of the DBFLN. 2017 Conference Paper http://hdl.handle.net/20.500.11937/52864 10.1109/SSCI.2016.7850114 restricted |
| spellingShingle | Savitha, R. Chan, Kit Yan San, P. Ling, S. Suresh, S. A hybrid Deep Boltzmann Functional Link Network for classification problems |
| title | A hybrid Deep Boltzmann Functional Link Network for classification problems |
| title_full | A hybrid Deep Boltzmann Functional Link Network for classification problems |
| title_fullStr | A hybrid Deep Boltzmann Functional Link Network for classification problems |
| title_full_unstemmed | A hybrid Deep Boltzmann Functional Link Network for classification problems |
| title_short | A hybrid Deep Boltzmann Functional Link Network for classification problems |
| title_sort | hybrid deep boltzmann functional link network for classification problems |
| url | http://hdl.handle.net/20.500.11937/52864 |