Denison D.D., Hansen M.H., Holmes C.C., Mallick B., Yu B. (eds.) Nonlinear Estimation and Classification

New York: Springer, 2003. — 465 p.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.Wavelet Statistical Models and Besov Spaces
Coarse-to-Fine Classification and Scene Labeling
Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Sets
Traffic Flow on a Freeway Network
Internet Traffic Tends Toward Poisson and Independent as the Load Increases
Regression and Classification with Regularization
Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines
The Boosting Approach to Machine Learning: An Overview
Improved Class Probability Estimates from Decision Tree Models
Gauss Mixture Quantization: Clustering Gauss Mixtures
Extended Linear Modeling with SplinesAdaptive Sparse Regression
Multiscale Statistical Models
Wavelet Thresholding on Non-Equispaced Data
Multi-Resolution Properties of Semi-Parametric Volatility Models
Confidence Intervals for Logspline Density Estimation
Mixed-Effects Multivariate Adaptive Splines Models
Statistical Inference for Simultaneous Clustering of Gene Expression Data
Statistical Inference for Clustering MicroarraysLogic Regression — Methods and Software
Adaptive Kernels for Support Vector Classification
Generalization Error Bounds for Aggregate Classifiers
Risk Bounds for CART Regression Trees
On Adaptive Estimation by Neural Net Type Estimators
Nonlinear Function Learning and Classification Using RBF Networks with Optimal Kernels
Instability in Nonlinear Estimation and Classification: Examples of a General Pattern
Model Complexity and Model Priors
A Strategy for Compression and Analysis of Very Large Remote Sensing Data Sets
Targeted Clustering of Nonlinearly Transformed Gaussians
Unsupervised Learning of Curved Manifolds
ANOVA DDP Models: A Review