The self-organizing map (SOM) is naturally unsupervised learning, but if a class label is known, it can be used as the classifier. In a SOM classifier, each neuron is assigned a class label based on the maximum class frequency and classified by a nearest neighbor strategy. The drawback when using this strategy is that each pattern is treated by equal importance in counting class frequency regardless of its typicalness. For this reason, the fuzzy class membership can be used instead of crisp class frequency and this fuzzy membership-label neuron provides another perspective of a feature map. This fuzzy class membership can be also used to select training samples in a support vector machine (SVM) classifier. This method allows us to reduce the training set as well as support vectors without significant loss of classification performance.
S. Sohn and C. H. Dagli, "Advantages of using Fuzzy Class Memberships in Self-Organizing Map and Support Vector Machines," Proceedings of the International Joint Conference on Neural Networks, 2001, Institute of Electrical and Electronics Engineers (IEEE), Jan 2001.
The definitive version is available at https://doi.org/10.1109/IJCNN.2001.938451
International Joint Conference on Neural Networks, 2001
Engineering Management and Systems Engineering
Keywords and Phrases
Class Label; Classification Performance; Classifier; Feature Map; Fuzzy Class Memberships; Fuzzy Set Theory; Learning Automata; Maximum Class Frequency; Nearest Neighbor Strategy; Pattern Classification; Self-Organising Feature Maps; Self-Organizing Map; Support Vector Machines; Training Samples; Unsupervised Learning
Article - Conference proceedings
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