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|Title:||Unsupervised learning by examples: On-line versus off-line||Authors:||VAN DEN BROECK, Christian||Issue Date:||1998||Publisher:||SPRINGER-VERLAG SINGAPORE PTE LTD||Source:||Wong, KYM. & King, I. & Yeung, DY. (Ed.) THEORETICAL ASPECTS OF NEURAL COMPUTATION - A MULTIDISCIPLINARY PERSPECTIVE. p. 249-255.||Abstract:||We study both on-line and off-line learning in the following unsupervised learning scheme: p patterns are sampled independently from a distribution on the N-sphere with a single symmetry breaking orientation. Exact results are obtained in the limit p --> infinity and N --> infinity with finite ratio p/N. One finds that for smooth pattern distributions, the asymptotic behavior of the optimal off-line and on-line learning are identical, and saturate the Cramer-Rao inequality from statistics. For discontinuous pattern distributions on the other hand, the optimal online algorithm needs (at least) twice as many examples asymptotically to reach the optimal off-line performance.||Notes:||Limburgs Univ Ctr, B-3590 Diepenbeek, Belgium.||Document URI:||http://hdl.handle.net/1942/9055||ISI #:||000078895600023||Type:||Proceedings Paper||Validations:||ecoom 2000|
|Appears in Collections:||Research publications|
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