Nonlinear Estimation and Classification by David D. Denison (English) Paperback

Description: Nonlinear Estimation and Classification by David D. Denison, Mark H. Hansen, Christopher C. Holmes, Bani Mallick, Bin Yu This book presents research on analyzing massive amounts of high-dimensional and highly structured data. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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. Notes This book presents the latest research on analyzing massive amounts of high-dimensional and highly structured data. Table of Contents I Longer Papers.- 1 Wavelet Statistical Models and Besov Spaces.- 2 Coarse-to-Fine Classification and Scene Labeling.- 3 Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Sets.- 4 Traffic Flow on a Freeway Network.- 5 Internet Traffic Tends Toward Poisson and Independent as the Load Increases.- 6 Regression and Classification with Regularization.- 7 Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines.- 8 The Boosting Approach to Machine Learning: An Overview.- 9 Improved Class Probability Estimates from Decision Tree Models.- 10 Gauss Mixture Quantization: Clustering Gauss Mixtures.- 11 Extended Linear Modeling with Splines.- II Shorter Papers.- 12 Adaptive Sparse Regression.- 13 Multiscale Statistical Models.- 14 Wavelet Thresholding on Non-Equispaced Data.- 15 Multi-Resolution Properties of Semi-Parametric Volatility Models.- 16 Confidence Intervals for Logspline Density Estimation.- 17 Mixed-Effects Multivariate Adaptive Splines Models.- 18 Statistical Inference for Simultaneous Clustering of Gene Expression Data.- 19 Statistical Inference for Clustering Microarrays.- 20 Logic Regression — Methods and Software.- 21 Adaptive Kernels for Support Vector Classification.- 22 Generalization Error Bounds for Aggregate Classifiers.- 23 Risk Bounds for CART Regression Trees.- 24 On Adaptive Estimation by Neural Net Type Estimators.- 25 Nonlinear Function Learning and Classification Using RBF Networks with Optimal Kernels.- 26 Instability in Nonlinear Estimation and Classification: Examples of a General Pattern.- 27 Model Complexity and Model Priors.- 28 A Strategy for Compression and Analysis of Very Large Remote Sensing Data Sets.- 29 Targeted Clustering of Nonlinearly Transformed Gaussians.- 30Unsupervised Learning of Curved Manifolds.- 31 ANOVA DDP Models: A Review. Promotional Springer Book Archives Long 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 newproblems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern Feature Includes supplementary material: sn.pub/extras Description for Sales People This book presents the latest research on analyzing massive amounts of high-dimensional and highly structured data. Details ISBN0387954716 Publisher Springer-Verlag New York Inc. Series Lecture Notes in Statistics Year 2003 ISBN-10 0387954716 ISBN-13 9780387954714 Format Paperback Imprint Springer-Verlag New York Inc. Place of Publication New York, NY Country of Publication United States Edited by Bani Mallick DEWEY 519.5 Short Title NONLINEAR ESTIMATION & CLASSIF Language English Media Book Series Number 171 Publication Date 2003-01-22 Edition 2003rd Pages 477 Illustrations VII, 477 p. DOI 10.1007/b89200;10.1007/978-0-387-21579-2 AU Release Date 2003-01-22 NZ Release Date 2003-01-22 US Release Date 2003-01-22 UK Release Date 2003-01-22 Author Bin Yu Edition Description 2003 ed. Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96305856;

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