2020 | OriginalPaper | Buchkapitel
Support Vector Machine Optimized by Fireworks Algorithm for Handwritten Digit Recognition
Autoren: Eva Tuba, Romana Capor Hrosik, Adis Alihodzic, Raka Jovanovic, Milan Tuba
Erschienen in:
Modelling and Development of Intelligent Systems
Abstract
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.
