Browsing by Author "Hsu, YT"

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Grey self-organizing feature maps
(2002-10-01)
Hu, YC
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Chen, RS
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Hsu, YT
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Tzeng, GH
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科技管理研究所
In each training iteration of the self-organizing feature maps (SOFM), the adjustable output nodes can be determined by the neighborhood size of the winning node. However, it seems that the SOFM ignores some important information, which is the relationships that actually exist between the input training data and each adjustable output node, in the learning rule. By viewing input data and each adjustable node as a reference sequence and a comparative sequence, respectively, the grey relations between these sequences can be seen. This paper thus incorporates the grey relational coefficient into the learning rule of the SOFM, and a grey clustering method, namely the GSOFM, is proposed. From the simulation results, we can see that the best result of the proposed method applied for analysis of the iris data outperforms those of other known unsupervised neural network models. Furthermore, the proposed method can effectively solve the traveling salesman problem. (C) 2002 Elsevier Science B.V. All rights reserved.
Learning concepts by arranging appropriate training order
(2001-08-01)
Hsu, YT
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Hong, TP
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Tseng, SS
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資訊工程學系
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Department of Computer Science
Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine learning and an order arrangement scheme is proposed. The second step is to modify a conventional noise-free learning algorithm, thus making it suitable for noisy environment. The generalized version space learning algorithm is then adopted to process the training instances for deriving good concepts. Finally, experiments on the Iris Flower problem show that the new scheme can produce a good training order, allowing the generalized version space algorithm to have a satisfactory learning result.