Distance Invariant Self-organizing Map
博士 === 臺灣大學 === 資訊工程學研究所 === 98 === This dissertation presents a distance invariant manifold that preserves neighboring relationships among data patterns. Since all input patterns have their corresponding cells in the manifold space, the neighboring cells of the input pattern resembles that of the o...
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| Format: | Others |
| Language: | en_US |
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2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/33410109389825158018 |
| Summary: | 博士 === 臺灣大學 === 資訊工程學研究所 === 98 === This dissertation presents a distance invariant manifold that preserves neighboring relationships among data patterns. Since all input patterns have their corresponding cells in the manifold space, the neighboring cells of the input pattern resembles that of the output patterns. The manifold is invariant under the translation, rotation and scale of the pattern coordinates. And the neighboring relationships among cells are adjusted and improved in each iteration according to the algorithm of reduction of the distance preservation energy.
This dissertation also extends the algorithm to presents a MLP kernel. It maps all patterns in a one class into a single point in the output layer space and maps different classes into different points. These widely separated class points can be used for further classifications. The kernel is a layered feed-forward network. Each layer is trained using class differences and is trained independently layer after layer using a bottom-up construction. The value of class labels are not used in the training process. Therefore, this kernel can be used in separating multiple classes.
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