Entropy-Based Generation of Supervised Neural Networks for Classification of Structured Patterns  

Oleh:

Lee, Shie-Jue ; Tsai, Hsien-Leing

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 2 (Mar. 2004), page 283-297.
Topik: entropy; entropy - based; generation; neural networks; classification; structured patterns

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Sperduti and Starita proposed a new type of neural network which consists of generalized recursive neurons for classification of structures. In this paper, we propose an entropy - based approach for constructing such neural networks for classification of acyclic structured patterns. Given a classification problem, the architecture, i. e., the number of hidden layers and the number of neurons in each hidden layer, and all the values of the link weights associated with the corresponding neural network are automatically determined. Experimental results have shown that the networks constructed by our method can have a better performance, with respect to network size, learning speed, or recognition accuracy, than the networks obtained by other methods.

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