Campos, Marcos M.Carpenter, Gail2011-11-142011-11-141997-05https://hdl.handle.net/2144/2119The WSOM (Wavelet Self-Organizing Map) model, a neural network for the creation of wavelet bases adapted to the distribution of input data, is introduced. The model provides an efficient on-line way to construct high-dimensional wavelet bases. Simulations of a lD function approximation problem illustrate how WSOM adapts to non-uniformly distributed input data, outperforming the discrete wavelet transform. A speaker-independent vowel recognition benchmark task demonstrates how the model constructs high-dimensional bases using low-dimensional wavelets.en-USCopyright 1997 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.Technical ReportBoston University Trustees