Support Vector Machine Optimized by Fireworks Algorithm for Handwritten Digit Recognition

  • Eva Tuba
  • Romana Capor Hrosik
  • Adis Alihodzic
  • Raka Jovanovic
  • Milan TubaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1126)


Handwritten digit recognition is an important subarea in the object recognition research area. Support vector machines represent a very successful recent binary classifier. Basic support vector machines have to be improved in order to deal with real-world problems. The introduction of soft margin for outliers and misclassified samples as well as kernel function for non linearly separably data leads to the hard optimization problem of selecting parameters for these two modifications. Grid search which is often used is rather inefficient. In this paper we propose the use of one of the latest swarm intelligence algorithms, the fireworks algorithm, for the support vector machine parameters tuning. We tested our approach on standard MNIST base of handwritten images and with selected set of simple features we obtained better results compared to other approaches from literature.


Handwritten digit recognition Machine learning Support vector machine Optimization Swarm intelligence Fireworks algorithm 



This research was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Grant No. III-44006.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Eva Tuba
    • 1
  • Romana Capor Hrosik
    • 2
  • Adis Alihodzic
    • 3
  • Raka Jovanovic
    • 4
  • Milan Tuba
    • 1
    Email author
  1. 1.Singidunum UniversityBelgradeSerbia
  2. 2.Maritime DepartmentUniversity of DubrovnikDubrovnikCroatia
  3. 3.Faculty of ScienceUniversity of SarajevoSarajevoBosnia and Herzegovina
  4. 4.Qatar Environment and Energy Research Institute (QEERI), Hamad bin Khalifa UniversityDohaQatar

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