Nonlinear Estimation and Classification | 
 enlarge | Authors: M. H. Hansen, C. Holmes, B. Mallick, B. Yu
Creator: D. D. Denison
Publisher: Springer
Category: Book
Buy Used: $153.90
Used (2) from $153.90
Sales Rank: 4925309
Media: Paperback
Edition: 1
Pages: 432
Number Of Items: 1
Shipping Weight (lbs): 1.5
Dimensions (in): 9.3 x 6.3 x 0.9
ISBN: 0387954716
Dewey Decimal Number: 519.544
EAN: 9780387954714
Publication Date: January 22, 2003
Availability: Usually ships in 1-2 business days
Shipping: Expedited shipping available
Condition: same ISBN / cover; 2003 copyright; some wear / cover wear; text clean / very good
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Product Description
Researchers in many disciplines now face the formidable task of processing massive amounts of high-dimensional and highly structured data. Advances in data collection and information technologies have coupled with innovations in computing to make commonplace the task of learning from complex data. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the difficulty of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern s, a term that we liberally interpret to include speech and pattern recognition, classification, data compression and image processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. This volume collects 31 papers from a unique workshop designed to promote communication between these different disciplines. Held in the spring of 2001 at the Mathematical Sciences Research Institute in Berkeley, CA, the meeting brought together eminent experts from machine learning, artificial intelligence, applied mathematics, image analysis, signal processing, information theory, and optimization. In addition to presentations on fundamental methodological work, there were talks on complex applications like environmental modeling, network analysis, and bioinformatics. Statistics
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