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- W259603650 abstract "In this paper we address the two major limitations of standard Self Organiz- ing Maps algorithm: i) The standard SOM finds different similarities among the sample vectors each time the initial conditions are changed. ii) As the input space can be com- plicated with a dimension larger than that of the feature map, the dimension reduction in SOM often generates a folded feature map. To cope with these limitations, we pro- pose the following: 1. Extend the neighborhood concept on the unit space by combining Kohonen's neighbors with a new set of units that we select in a fashionable manner; 2. Introduce a complementary learning rule that involves the information acquired by the net during training, and which involves the network internal structure; 3. We make Kohonen's neighborhood function interactive with the learning progress according to the training instant needs by assigning to each unit of the feature map a pseudo-neighborhood function, which is a customized version of Kohonen's one; 4. We establish an interactive training procedure in which learning is not performed the same way during the whole training process, as in standard SOM. The established novel learning algorithm makes the resultant mapping not only invariant to the initial conditions, but also free of all folding that may occur during learning. Keywords: Self organizing maps, Neighborhood function, Initial conditions, Border effect, Folding effect" @default.
- W259603650 created "2016-06-24" @default.
- W259603650 creator A5073328436 @default.
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- W259603650 date "2006-01-01" @default.
- W259603650 modified "2023-09-26" @default.
- W259603650 title "INTERACTIVE ADAPTATION/CORRECTION BEHAVIOR BY INVOLVING SOM INTERNAL STRUCTURE IN LEARNING PROCESS" @default.
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