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Feature space learning in Support Vector Machines through Dual Objective optimization

Pietersma, A.D (2010) Feature space learning in Support Vector Machines through Dual Objective optimization. Master's Thesis / Essay, Artificial Intelligence.

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In this study we address the problem on how to more accurately learn underlying functions describing our data, in a Support Vector Machine setting. We do this through Support Vector Machine learning in conjunction with a Weighted- Radial-basis function. The Weighted-Radial-Basis function is similar to the Radial-Basis function, in addition it has the ability to perform feature space weighing. By weighing each feature differently we overcome the problem that every feature supposedly has equal importance for learning ourk target function. In order to learn the best feature space we designed a feature-variance filter. This filter scales the feature space dimensions according to the relevance each dimension has for the target function, and was derived from the Support Vector Machine's dual objective -definition of the maximum-margin hyperplane-with the Weighted-Radial-Basis function as a kernel.The "fitness'' of the obtained feature space is determined by its costs, where we view the SVMs dual objective as a cost function.Using the newly obtained feature space we are able to more precisely learn feature spaces, and thereby increase the classification performance of the Support Vector Machine.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:44
Last Modified: 15 Feb 2018 07:44

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