Guaranteed Two-Pass Convergence for Supervised and Inferential Learning  

Oleh:

Caudell, T. P. ; Healy, M. J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 1 (1998), page 195-204.
Topik: LEARNING; two - pass convergence; inferential; learning

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We present a theoretical analysis of a version of the LAPART adaptive inferencing neural network. Our main result is a proof that the new architecture, called LAPART 2, converges in two passes through a fixed training set of inputs. We also prove that it does not suffer from template proliferation. For comparison, Georgiopoulos et al. (1994) have proved the upper bound n - 1 on the number of passes required for convergence for the ARTMAP architecture, where n is the size of the binary pattern input space. If the ARTMAP result is regarded as an n - pass, or finite - pass, convergence result, ours is then a two - pass, or fixed - pass, convergence result. Our results have added significance in that they apply to set- v alued mappings, as opposed to the usual supervised learning model of affixing labels to classes.

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