Nonlinear Estimation and Classification

2003
ISBN: 978-0-387-95471-4
Verlag: Springer

Buch, Englisch, 477 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 733 g

Reihe: Lecture Notes in Statistics

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.

Zielgruppe

Research

Autoren/Hrsg.

Denison, David D.

Hansen, Mark H.

Yu, Bin

Holmes, Christopher C.

Mallick, Bani

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Mathematische Analysis Moderne Anwendungen der Analysis
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

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.