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Deep Support Vector Machines for Classification and Autoencoding Problems

Mark, R.W. van der (2013) Deep Support Vector Machines for Classification and Autoencoding Problems. Bachelor's Thesis, Artificial Intelligence.

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Abstract

Although the single-layer support vector machine is a popular machine learning method, previous research showed that the performance of the SVM for regression problems can be improved by adding a second layer of lower-level SVMs for feature extraction from the input patterns. In this thesis, a method for the application of the deep support vector machine for classification and autoencoding problems is presented. A two-layer support vector machine is compared to a regular SVM on eight classification datasets, and the results show that the DSVM outperforms the SVM on half of the tested datasets. For autoencoding problems, the DSVM is compared to a regular neural network autoencoder and a neural support vector machine. The results show that the DSVM also outperforms these methods.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 07:54
Last Modified: 15 Feb 2018 07:54
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/11240

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