Cooperation of support vector machines for handwritten digit recognition trough partitioning of the feature set
Date Issued
2003
Author(s)
Gjorgjevikj, Dejan
Chakmakov, Dushan
Abstract
In this paper, various cooperation
schemes of SVM (Support Vector Machine)
classifiers applied on two feature sets for
handwritten digit recognition are examined.
We start with a feature set composed of
structural and statistical features and corresponding SVM classifier applied on the complete feature set. Later, we investigate the various partitions of the feature set as well as the
advantages and weaknesses of various decision fusion schemes applied on SVM classifiers
designed for partitioned feature sets. The obtained results show that it is difficult to exceed the recognition rate of a single SVM classifier applied straightforwardly on the complete feature set. Additionally, we show that the
partitioning of the feature set according to
feature nature (structural and statistical features) is not always the best way for designing
classifier cooperation schemes. These results
impose need of special feature selection procedures for optimal partitioning of the feature set for classifier cooperation schemes.
schemes of SVM (Support Vector Machine)
classifiers applied on two feature sets for
handwritten digit recognition are examined.
We start with a feature set composed of
structural and statistical features and corresponding SVM classifier applied on the complete feature set. Later, we investigate the various partitions of the feature set as well as the
advantages and weaknesses of various decision fusion schemes applied on SVM classifiers
designed for partitioned feature sets. The obtained results show that it is difficult to exceed the recognition rate of a single SVM classifier applied straightforwardly on the complete feature set. Additionally, we show that the
partitioning of the feature set according to
feature nature (structural and statistical features) is not always the best way for designing
classifier cooperation schemes. These results
impose need of special feature selection procedures for optimal partitioning of the feature set for classifier cooperation schemes.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
ETAI2003-I4-2.pdf
Size
511.82 KB
Format
Adobe PDF
Checksum
(MD5):2b2268ad9dc80854bde2b056f7f62a9a