Year: 2016
Type: article
Abstract: Handwritten digit recognition is an important but very hard practical problem. This is a classification problem\nfor which support vector machines are very successfully used. Determining optimal support vector machine is\nanother hard optimization problem that involves tuning of the soft margin and kernel function parameters. For this\noptimization we adjusted recent swarm intelligence bat algorithm. We intentionally used weak set of features, four\nhistogram projections, to prove that even under unfavorable conditions our algorithm would achieve acceptable\nresults. We tested our approach on standard MNIST benchmark datasets and compared the results with other recent\napproaches from literature where our proposed algorithm achieved better results i.e. higher correct classification\npercentage.
Authors: Eva Tuba, Milan Tuba, Dana Simian
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G3382203950, G570082566
OpenAlex ID: W2927356562