Nonlinear Estimation and Classification

Table of Contents

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   Book Overview
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   Chapter 1 Introduction
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   Chapter 2 Wavelet Statistical Models and Besov Spaces
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   Chapter 3 Coarse-to-Fine Classification and Scene Labeling
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   Chapter 4 Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Sets
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   Chapter 5 Traffic Flow on a Freeway Network
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   Chapter 6 Internet Traffic Tends Toward Poisson and Independent as the Load Increases
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   Chapter 7 Regression and Classification with Regularization
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   Chapter 8 Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines
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    Chapter 9 The Boosting Approach to Machine Learning: An Overview
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    Chapter 10 Improved Class Probability Estimates from Decision Tree Models
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    Chapter 11 Gauss Mixture Quantization: Clustering Gauss Mixtures
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    Chapter 12 Extended Linear Modeling with Splines
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    Chapter 13 Adaptive Sparse Regression
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    Chapter 14 Multiscale Statistical Models
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    Chapter 15 Wavelet Thresholding on Non-Equispaced Data
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    Chapter 16 Multi-Resolution Properties of Semi-Parametric Volatility Models
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    Chapter 17 Confidence Intervals for Logspline Density Estimation
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    Chapter 18 Mixed-Effects Multivariate Adaptive Splines Models
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    Chapter 19 Statistical Inference for Simultaneous Clustering of Gene Expression Data
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    Chapter 20 Statistical Inference for Clustering Microarrays
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    Chapter 21 Logic Regression — Methods and Software
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    Chapter 22 Adaptive Kernels for Support Vector Classification
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    Chapter 23 Generalization Error Bounds for Aggregate Classifiers
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    Chapter 24 Risk Bounds for CART Regression Trees
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    Chapter 25 On Adaptive Estimation by Neural Net Type Estimators
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    Chapter 26 Nonlinear Function Learning and Classification Using RBF Networks with Optimal Kernels
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    Chapter 27 Instability in Nonlinear Estimation and Classification: Examples of a General Pattern
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    Chapter 28 Model Complexity and Model Priors
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    Chapter 29 A Strategy for Compression and Analysis of Very Large Remote Sensing Data Sets
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    Chapter 30 Targeted Clustering of Nonlinearly Transformed Gaussians
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    Chapter 31 Unsupervised Learning of Curved Manifolds
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    Chapter 32 ANOVA DDP Models: A Review