Abstract
Self-organizing map (SOM) is naturally unsupervised learning, but if a class label is known, it can be used as the classifier. In 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 support vector machines (SVM) classifier. This method allows us to reduce the training set as well as support vectors without significant loss of classification performance.
Original language | English (US) |
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Pages | 1886-1890 |
Number of pages | 5 |
State | Published - 2001 |
Event | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: Jul 15 2001 → Jul 19 2001 |
Other
Other | International Joint Conference on Neural Networks (IJCNN'01) |
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Country/Territory | United States |
City | Washington, DC |
Period | 7/15/01 → 7/19/01 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence