VLADIMIR CHERKASSKY; FILIP MULIER – LEARNING FROM DATA. CONCEPTS; THEORY & METHODS

The principles and methods for learning from data are covered in this book. It shows that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. The text is complete with over one hundred illustrations and case studies.

TABLE OF CONTENTS

PREFACE.

NOTATION.

There is an introduction.

Learning and Statistical Estimation.

There is a statistical dependency and causality.

Variables are Characterization of Variables.

Characterization of uncertainty

There are other data analytical Methodologies.

There are two problem statements, classical approaches and adaptive learning.

The learning problem is formulated.

The objective of learning.

There are two common learning tasks.

There is a scope of the learning problem formulation.

There are 2.2 Classical Approaches.

The Density Estimation.

The classification is 2.2.2

There is a regression.

There are problems with finite data.

There are 2. 2.5 nonparametric methods.

There is a Stochastic Approximation.

There are concepts andctive principles in adaptive learning.

There are major concepts and issues.

There is a priori knowledge and model complexity.

The principles ofctive.

Alternative learning formulas.

There is a summary.

There is a framework for regularization.

There is a curse and complexity of dimensions.

Approximation and Characterization of Complexity is a function.

Penalization.

Penalties under the Parametric Penalties.

There are 3 nonparametric Penalties.

The model selection is related to complexity control.

The criteria for the selection of analytical models.

Resampling is used for model selection.

There is a Bias–Variance Tradeoff.

There is an example of model selection.

Function Approximation versus Predictive Learning.

There is a summary.

There is a theory of Statistical Learning.

There are conditions for consistency and convergence.

Growth function and VC dimensions.

The VC is used for classification and regression problems.

There are examples of calculating VC dimensions.

There are bounds on the generalization.

There is a classification.

There is a regression.

Generalization Bounds and Sampling Theorem are related.

Structural Risk Minimization is related to structural risk.

The Representation of a Dictionary.

There is a feature selection.

Penalization formulation

Input Preprocessing

There are initial conditions for training.

There are comparisons of model selection for regression.

There is a model selection for linear estimators.

There is a model selection for k-Nearest-Neighbor Regression.

There is a model selection for Linear Subset Regression.

There is a 4.5.4 discussion.

The VC dimensions are measured.

There are four things: VC Dimension, OcCAM’s Razor, and Popper’s Falsifiability.

There was a summary and discussion.

There are 5 strategies for Nonlinear Optimization.

Approximation methods using the Stochastic method.

There is a Linear Parameter Estimation.

The training of the networks.

Iterative methods are used.

There are methods for Density Estimation.

There is generalized inverse training of the networks.

There is Greedy Optimization.

Neural Network Construction Algorithms are used.

There are classifications and regression trees.

Theory Selection, Optimization, and Statistical Learning are related.

There is a summary.

There are 6 methods for reducing data.

There is a method of quantization and clustering.

Optimal source coding is used in quantization.

The Lloyd Algorithm is Generalized.

There is a clustering of things.

VQ and Clustering are covered by the 6.1.4 EM Algorithm.

There is a Fuzzy Clustering.

Statistical methods for reducing dimensions.

There are Linear Principal Components.

There are Principal Curves and Surfaces.

Multidimensional scaling.

Neural network methods for reduction of dimensions.

There is a self-Organizing Map Algorithm.

Statistical Interpretation of the SOM Method.

The SOM and Learning Rate Schedules have a flow-through version.

Modifications and applications.

There is a Self-Supervised MLP.

There are methods for analyzing data.

Factor analysis

The analysis is called the Independent Component Analysis.

There is a summary.

There are 7 methods for regression.

Dictionary versus Kernel Representation is a Taxonomy.

There are 7.2 Linear Estimators.

Estimation of Linear Models and Equivalence of Representations.

There is alytic form of cross validation.

Estimating Complexity of Penalized Linear Models

There are nonadaptive methods.

The methods of the adaptive dictionary.

Projection Pursuit Regression and Additive Methods.

There are multilayer perceptrons and backpropagation.

There are multivariate regression lines.

There are orthogonal Basis Functions and Wavelet Signal Denoising.

Local risk minimization is one of the methods adaptive kernels use.

Generalized memory-based learning is a type of learning.

There is a Constrained Topological Mapping.

There are 7.5 empirical studies.

Predicting net asset value of mutual funds

There is a comparison of methods for regression.

Combining models.

There is a summary.

The classification is 8.

Statistical Learning Theory Formulation is a topic.

Classical formulation.

Statistical Decision Theory is a field of study.

The Linear Discriminant Analysis was written by Fisher.

There are methods for classification.

There are regression-based methods.

The methods are tree-based.

There are Nearest-Neighbor and Prototype Methods.

There are Empirical Comparisons.

Methods and boosting are combined.

Increasing as an Additive Model.

There is a boost for regression problems.

There is a summary.

There are 9 support Vector Machines.

There is motivation for margin-based loss.

There are Margin-Based Loss, Robustness, and Complexity Control.

There is an optimal Separating Hyperplane.

There are high-dimensional mapping and inner product kernels.

There is support for a machine for classification.

There are support Vector Implementations.

There is support for regression.

There is a model selection.

There are support machines for regularization.

There is a single class of detection.

There was a summary and discussion.

There are 10 noninductive inference and alternative learning formulas.

There is high-dimensional data.

Transduction.

ference through contradiction

There is a multiple-model estimation.

There is a summary.

There are 11 concluding remarks.

There is a review of Nonlinear Optimization in Appendix A.

Singular Value Decomposition and Eigenvalues are included in Appendix B.

There are references.

Index.

AUTHOR INFORMATION

Professor CherKassky is at the University of Minnesota. He is known for his research on neural networks.

For the last twelve years, Mulier has worked in the software field, researching, developing, and applying advanced statistical and machine learning methods. He has a project management position.

REVIEWS

I think so. Learning from data. It’s a very valuable volume. I will recommend it to my graduate students. The American Statistical Association has a journal. March 2009.

The broad spectrum of information it offers is beneficial to many field of research. Many researchers and practioners will find this book useful because of the good selection of topics. It’s called technometrics. May 2008)

Some of the recent trends and future challenges in different learning methods have been summarized by the authors. Computing reviews. May 22, 2008)

Vladimir Cherkassky and Filip Mulier are learning from data. Theory and methods.

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