Structure-adaptive SOM to classify 3-dimensional point light actors' gender

Classifying the patterns of moving point lights attached on actor's bodies with self-organizing map often fails to get successful results with its original unsupervised learning algorithm. This paper exploits a structure-adaptive self-organizing map (SASOM) which adaptively updates the weights,...

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Published inICONIP '02 : proceedings of the 9th International Conference on Neural Information Processing : computational intelligence for the E-age : November 18-22, 2002, Singapore Vol. 2; pp. 949 - 953 vol.2
Main Author Sung-Bae Cho
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Backpropagation algorithms
Computer science
Displays
Electronic mail
Humans
Machine learning
Neural networks
Organizing
Pattern recognition
Testing
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Summary:Classifying the patterns of moving point lights attached on actor's bodies with self-organizing map often fails to get successful results with its original unsupervised learning algorithm. This paper exploits a structure-adaptive self-organizing map (SASOM) which adaptively updates the weights, structure and size of the map, resulting in remarkable improvement of pattern classification performance. We have compared the results with those of conventional pattern classifiers and human subjects. SASOM turns out to be the best classifier producing 97.1% of recognition rate on the 312 test data from 26 subjects.
ISBN:9810475241
9789810475246
DOI:10.1109/ICONIP.2002.1198201