Nonlinear Estimation and Classification [electronic resource] /

Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.

Bibliographic Details

Main Authors:	Denison, David D. editor., Hansen, Mark H. editor., Holmes, Christopher C. editor., Mallick, Bani. editor., Yu, Bin. editor., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	New York, NY : Springer New York : Imprint: Springer, 2003
Subjects:	Statistics., Statistical Theory and Methods.,
Online Access:	http://dx.doi.org/10.1007/978-0-387-21579-2
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