Ree, M.H. van der (2011) The Neural Support Vector Machine. Bachelor's Thesis, Artificial Intelligence.
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Abstract
In this paper we describe a new training algorithm which can be used for both classification and regression problems. The Neural Support Vector Machine (NSVM) is a hybrid system whose architecture includes both neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs who take a central feature layer as their input. This feature layer is in turn the output of a number of neural networks who are trained to minimize the objectives of the SVMs. Since the system is able to handle multiple outputs, it can also be used as a dimensionality reduction method. The results of the conducted experiments indicate that the system performs on par with state-of-the-art classification and regression algorithms. NSVMs with a large feature layer seem able to outperform autoencoders on dimensionality reduction.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:46 |
| Last Modified: | 15 Feb 2018 07:46 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/9687 |
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